Modernisation of Operations Management: Role of ITS in Bus Operations at NMMT and the Netherlands

Introduction

Management and operations in transportation systems is defined as an “integrated approach to optimize the performance of the existing infrastructure through implementation of multi-modal, cross-jurisdictional systems, services and projects” (FHWA, 2013). It focuses on the transit vehicle operations directly and how they interact with the transit users. Increasing the performance of an existing infrastructure can improve operational performance, reduce long-term costs and save time (Abou-Senna et al, 2018). The components under operational systems are (ADB and MoUD, 2008; COST, 2011):

  • Route planning
  • Capacity augmentation
  • Ticketing, fare collection and revenue management
  • Operations management (Schedule span, type of services, driving rules, etc.)
  • Customer’s orientation
  • Passenger information
  • Operator’s efficiency
  • Human resource development
  • Quality Management (including safety, security, operator’s training, etc.)

It is important that the transport infrastructure always adapt to the constant growth of the city and its never-ending demand. Information Technology Services (ITS) provides many solutions and models that can help in data collection, forecasting the demand, tracking the vehicles and the passenger movement. All major cities, like Amsterdam, Sydney, Sao Paolo, London, etc. make extensive use of technology in their bus operations and maintenance. They have a centralised command centre and they track the buses through GPS (EMBARQ, 2010).

The benefits of management and operations strategies like these brings forth safer travel, reduced delay in commute, improved reliability, lesser wasted fuel, cleaner air, etc. (FHWA, 2017). Earlier, we have identified that Indian cities have started implementing ITS to help improve its transportation planning and management. In this article, we will study the data management and collection methods in practice at the Navi Mumbai Municipal Transport (NMMT) control centre.

Case Study 1 – Real-time Data Management at NMMT, Navi Mumbai

Currently, NMMT has a bus fleet of 467 buses running on 75 routes. It experiences a daily ridership of approximately 3 lac passengers and generates an approximate daily income of Rs. 37-40 lacs. All the bus lines add up to a total route length of 1895 kms. and have an average length of 26 kms. The average headway is about 15 minutes, the maximum being 65 minutes and a minimum of 7-10 minutes (“NMMT City Bus System”, 2017). NMMT has allocated the buses among 3 depots (Turbhe, Asudgaon and Ghansoli) and 13 bus terminals.

On similar grounds of other major cities mentioned earlier, NMMT has also established a centralised command centre. It tracks the daily movement in the buses to make its operations and maintenance more efficient. They have implemented the real-time data management system through these eight modules:

1.      Automatic Vehicle Locator System (AVLS)

AVLS captures the real-time on-board location and helps create a substantial database where the progress of the bus is stored on a second-to-second basis (Hounsell, Shrestha and Wong, 2012). It receives and stores the bus location and also the bus event information through an on-board GPS. Through this system, the location, speed and the route of the buses can be tracked. From the current location of the buses being tracked and comparing it with an average gives the estimated time to reach a destination. Through the same module, the estimated time for the bus to reach a bus-stop is also calculated.

Fig 1 – The total number of GPS enabled buses distributed among the three depots.

Over 95% of the buses have a GPS installed in them. GPS boxes in the older buses are being installed externally, while the newer buses come with an inbuilt GPS. Based on the movement of the bus, its status (Running, idle, on-trip standby, off-trip standby) gets constantly updated at the control centre, which is useful during the peak hours.

2.      Passenger Information System (PIS)

Deriving the information from AVLS, the control centre constantly tracks the real-time information of the buses.  It calculates the estimated arrival and travel time of the buses based on the historical travel data across different road segments and the time of the day. The commuters can receive this information (estimated arrival and travel time) through the mobile application. The passengers can also get information about the bus drivers and report for incidents.

The passenger movement is counted from the tickets count, through which the peak and off-peak hours are estimated. NMMT uses this information to dispatch the buses and at the same time maintain a reserve stock of them. The reserve stock is useful in case of unprecedented demand or breakdown of a bus.

3.      Control command centre

The control centre constantly records and analyses the real-time information of the buses and passenger’s commute. AVLS and PIS provides a substantial database, which is useful in the maintenance and operations of the buses. Based on the data provided, the control centre is able to:

  • Forecast demand
  • Avoid bus-bunching
  • Check the fare collection and segregating it according to different categories
  • Track the buses for route violations and over-speeding
  • Check for incident reports
  • Interact with the staff and the commuters
  • Maintain the database

Image 2 – The role of control center in real-time data management of NMMT. (Content source – Hounsell Shrestha and Wong, 2012)

4.      Incident Management

The control centre keeps a track of the bus operators and if their buses are following the route or not. They also maintain the incidence reports submitted by the commuters. In cases of any issue noticed by the centre or submitted by the commuter, the control centre resolves it immediately. Operational faults and break-downs are resolved by the respective depots, this:

  • Releases the work-load on a single depot
  • Allows depots to deploy reserve buses effectively
5.      Mobile application

Information like the schedule of the buses, its operators, etc. are available on the mobile application.  Through the mobile application, the commuters are capable of:

  • Checking the nearest bus-stops and routes
  • Checking the available buses and the waiting time
  • Setting a time for notification to leave their place of origin and reach the bus stops.
  • Checking the details of the bus and the bus operators
  • Reporting an incident
6.      Business Intelligence, Financial management system and Enterprise management system

The control centre creates different real-time reports for the general manager, the accounts department and the employees of NMMT. These reports help them to monitor and analyse the performance of the buses and the operating staff.

7.      Scheduling and planning

The scheduling of the buses at the initial stages follows the traditional approach by over-lapping On-site surveys, Activities according to the land-use maps and The number of buses available.

The number of buses on a particular route are increased or reduced according to the demand of the commuters. This demand is tracked online through the count of the tickets.

8.      Automatic Fare Collection System

There are many ways to register a trips made by the commuters; through on-board ticketing, monthly passes and through a mobile application. All of these are recorded and maintained to analyse the daily ridership in the buses. Through which, the peak and off-peak hours are estimated. The same online system is also used to create stock correction reports.

Case Study 2 – Network of Bus Corridors in the Netherlands

Any transportation system is based on potential user’s demand. This demand forms the technical foundations for designing the system, planning operations and the financial feasibility (EMBARQ, 2010). Route planning of any public transport should always be in response to the context of the neighborhood and in consultation with the local stakeholders. It should be laid out to serve the maximum commuters in the most efficient way.

Following a similar ideology, the development or improvement of the public transport in the Netherlands is done gradually (from a regular bus to a dedicated infrastructure) on the basis of the integral vision of the change in transport requirements (number of passengers) and the development of the locations (with the increase in number of residents and jobs) (Public transport in the Netherlands, 2016).

This data to document the necessity to develop a route is collected through many ITS models. An estimated amount of €170 million is budgeted for 75 projects in total; for data collection models such as cluster travel information, Multi-Modal information, dynamic traffic management, etc. (Ministry of Infrastructure and Environment, The Netherlands, 2012). The data is processed into travel information, for both unimodal and multimodal mode, through apps such as 9292 (public transportation) and ANWB (Dutch Automobile Club). The travel information is useful for improved accessibility and traffic flows. The appropriate use of ITS architecture leads to co-ordinated and standardised development of a cohesive framework of technical and information structures (Ministry of Infrastructure and Environment, The Netherlands, 2012).

The integration of different services is also one of the key features of Dutch public transport. It follows a hierarchy of fast (peak hour), local and community, and demand responsive services. The bus operators setup their time-tables around a ‘transfer-scheme’ to be able to find a convenient way to connect to a metro/rail. The ticketing and fare system is also integrated. Use of Strippenkaart, sterabonnement or ov-chipkaart (tickets and pre-paid cards) are capable to allow the commuters to travel using the same fare and tickets.

Take-Aways

The real-time data management system implemented in NMMT is still young and constantly upgrading. However, a positive impact in the operations can be seen. Since the implementation of this system, there has been a significant reduction in the incident reports (Fig 2). The statistics suggest that cases of over-speeding of buses is almost negligible now.

Fig 2 – Percentage reduction in incidence reports (Content source – NMMT)

Through constant tracking of the buses and implementation of this system, NMMT is now capable of:

  • Monitoring the services of the buses
  • Managing operational maintenance and reports
  • Real-time incidence reporting and resolving
  • Retrieving performance data for post-process applications
  • Reducing the manual data collection

Efficient data collection, availability of travel information and integration among different operators are key for developing an efficient operational model. A coherent and integrated route plan ensures user-friendliness and higher usage of the bus services. It has a direct influence on the passenger demand, reduced travel time and the operating costs; hence, also on the revenues (ADB and MoUD, 2008). Indian ULBs have also started developing similar models, however, the process of implementation is rather slower and complex. With an increasing use of ITS in bus operations, open data collection and disseminating travel information is getting easier and more efficient.

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Kirkpatrick Model – Four Level Training Evaluation Model

Kirkpatrick model is one of the highly recognized and widely used training evaluation model. It was developed by Dr. Donald L Kirkpatrick (1924-2014).  It is one of the most effective models to analyse and evaluate the results of educational programs.  It can objectively analyse the impact and efficacy of training. As it proceeds, the evaluation process gets more difficult and time consuming. However, the higher level assessments also generates information that is more critical and valuable.

The four levels of Kirkpatrick model (Source – kirkpatrickpartners.com)

By analysing each of these four levels, it is easier for a trainer to evaluate an effectiveness of training and find the ways to improve the future trainings. The four levels of the evaluation model are as follows:

  1. Reaction evaluation – Training participant’s opinion about the training and the trainer – The personal thoughts and the feelings are captured quantitatively through responses in a questionnaire (typically termed as ‘smile sheets’ or ‘happy sheets’). Questionnaire analyses the training content, methodology, facilities and the course content. Learners also respond to their first reaction to learning experience.
  2. Learning evaluation – The extent of learning after the training – It measures the personal development of the trainees by analyzing the increase in knowledge, the acquired skills or enhanced intellectual capabilities.This is assessed before and after (pre-test & post-test) the training so as to ascertain the scale at which learner has gained the knowledge. The evaluation involves observation and analysis of the voice, behaviour and text of the trainee. The measurement at this level gets more difficult and laborious as the participant’s evaluation moves from learner satisfaction to learner’s knowledge advancement.
  3. Behavioral change evaluation – The extent to which the trainees applied the acquired knowledge and changed their behavior. This change can be immediate or several months after the training depending on the situations. Behavior evaluation analyses the transfer of acquired knowledge from the training session to the work place. Here, the primary tool for evaluation is predominantly the observation. Apart from the observation, a combination of questionnaires and 360 Degree feedbacks are also used. It is rather difficult to predict the change in behavior and hence, the evaluation process gets even more difficult. It requires important decisions in terms of when and how the trainees should be evaluated.
  4. Result evaluation – To assess training in terms of business results. It is measured by assessing the change in key performance indicators of business which involves, achievement of standards and accreditations, number of complaints, profit and loss statements, business volumes, etc.  However, since all these factors are also affected by several other external factors it gets difficult to quantify the training impact on business results. This stage helps in identifying the ROI (Return on Investments) of the training.

In the context of trainings through UJJWAL at CIDCO, the training cell team captures the relevant information to evaluate the reaction of the trainees, which is the first level of the Kirkpatrick model. A feedback form that captures the reactions of the trainees is filled by them immediately after the training is over. 85-90% of the submitted participant’s feedbacks have already assessed the institute vis a vis faculty or the Subject Matter Expert (SME), relevance of the course, course content, training methods and other faculties. NIUA-CIDCO Smart city lab also incorporates the second level of the Kirkpatrick model. However, currently this is only being done for high end courses. At this stage, participants are asked to submit a brief on their learning in a pdf or doc version, so as to qualitatively assess the enhanced knowledge of the participant.

It is strongly believed that many of the participants in CIDCO have started implementing the knowledge gained during the training in their professional and personal lives. This can be evaluated through methods of psychometric assessments or 360 degree assessments in the third stage of this model. However, the tools to quantify the change in the application of the knowledge are still in development stages and can be assessed only in a larger group of participants over a period of 12-18 months. As the trainings gets more amenable in the coming months, UJJWAL’s training cell aims to take its evaluation process to the next stages of Kirkpatrick model. By doing so, it intends to measure a system wide impact measurable in terms of the people, the processes and the business of CIDCO.

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Engaging Through Online Platforms

INTRODUCTION

Throughout the world for many years, local bodies have been involved in deliberation of local issues, decision making within their capacities and choosing their leaders. The idea of citizen engagement in public affairs has been long prevalent (GCPSE, 2016). Similarly, the idea that computers and digital technologies can help us improve city in diverse ways, isn’t new either (Bollier, 2016). However, in the recent years there has been an increase in the number of citizen engagement activities and a shift is taking place from the top-down governance to a more horizontal process (Garrigues, 2017). With the changing trend, the policy makers have started looking for active citizen feedbacks to have a better sense of people’s priorities and to decide the need & shape of the public policy (Bollier, 2016; IPAT, 2015). The National Smart Cities Mission also identifies the importance of citizen engagement in the formation of a policy and actively works in applying it at different levels. In our previous newsletters we have discussed the strategies of citizen engagement taken up by different cities and a possible framework of process that can be implemented.  This article reflects on different case-studies around the world that initiated citizen engagement models on an online interface. It also reflects on their procedure and how they managed to derive an order in a situation of complexity.

Out of the SCPs of the 20 lighthouse cities there are many cities that understands the importance of using online platforms as an effective way of engaging citizens. Jaipur has come up with an online grievance redressal system app where the citizens can register any issues in their area. Surat has come up with many initiatives at different levels to ensure a comprehensive approach towards citizen engagement. Out of the many objectives, they have also developed an online citizen engagement platform for getting citizen’s feedback in decision making. Pune has created an ecosystem of around 400 partners to support the growing entrepreneurial culture and at the same time integrate the local stakeholders. Ahmedabad involves different groups of people, societies, working class to practice citizen consultation exercises for their inclusion.  There are many more examples to share.

FRAMEWORK OF OPERATIONS

Any citizen engagement process broadly involves three actors (GCPSE, 2016). They can then be further categorized according to their specialization:

  1. Decision makers – The politicians who aggregate the preferences of the citizens and facilitates the citizen’s expectations by deploying the resources and governance.
  2. Mediators – The public officials who deliver the public services to the citizens and implement the strategic direction of the policy decided by the politicians.
  3. Citizens – In a sort of ‘social contract’ with the politicians; gives the authority to the politicians and expects good public services in return. This also includes the local businesses and entrepreneurs.

The concept of citizen engagement requires an active dialogue between the citizens and the decision makers; it is not entirely similar to citizen participation (Garrigues, 2017). In citizen engagement, cities (or social systems) directly involve the citizens in the decision making process, it is a more formal structure that is organized by the public officials or the government. They do it by providing tools to consult and access public information, discuss with the elected representatives and monitor the implementations (Garrigues, 2017). Citizen engagement creates a sense of citizenship and educates the public in many ways (IPAT, 2015).  For an effective engagement process the public officials play an important role in mediating the preferences of the citizens and developing a network among the citizens with common interests. It is also very important the development model and the whole process is transparent. This is where an online platform can be of many utilities. Through the different case studies around Europe, we can develop an understanding on how they work.

INSIGHTS FROM THE CASE-STUDIES IN EUROPE

1)      ZO!city

The model was implemented in Amstel III which is the south-eastern neighborhood of Amsterdam. Post-2008 financial crisis, the area once had 25-30% vacant spaces (Beer, 2014). The existing stakeholders had limited contact with each other and cohesively lacked a sense of ownership.

The fragmented stakeholders were the key strength of the neighborhood. However, to setup a collaboration among them was the main challenge. The implementation of the model, initiated by Saskia Beer, was a step-by-step process:

  1. Analyse the neighborhood and identify the main strategic points where smaller interventions could’ve made a lot of difference.
  2. Informal meetings with the local stakeholders were held, which included real estate owners, companies, business associations, community organizations, etc. to understand the priorities, their willingness and their capacities to invest in the development model.
  3. Using metaphorical and non-technical language, the mediators created a manifesto that triggered the stakeholders to envision their own ideas and make the planning process seem more accessible to the citizens and the stakeholders.

Fig 1 – The three interconnected pillars of the development model

This was done by deliberately using a ‘light-hearted and positive’ campaign over a rather serious vision (Beer, 2014). The development model works on three interconnected pillars. The municipality simultaneously had its own objectives for the development model.

Saskia Beer initiated ‘glamourmanifest’ following the model of co-operation and co-creation with a collective instrument of interventions and investments. Implementing an adaptive practice to adjust to the changes and the opportunities that come along the way (Beer, 2014).

By collecting the ideas and the demands of the users, an urban vision was then synthesized. Initiatives and desires of the stakeholders were located on a map and overlapped. ‘High energy zones’ were identified where a lot of ideas and stakeholders overlapped.

It was realized that to establish an effective network of co-operation between the stakeholders it was very important that the information regarding the various initiatives is provided to them at ease. At the same time, a sense of transparency and availability of information is always required, to accommodate this need an online platform was launched and ‘glamourmanifest’ changed its name to ‘ZO!city’.

Fig 2 – The online interface of ZO!city

Fig 3 – On clicking the pins, the description of the ideas shows up

Response 

Anybody using the website has the liberty to suggest an initiative which is then opened for public voting and sources of funding. The initiatives are geo-located on a map which are also classified and colour-coded on the basis of its functionality. The ideas are then openly scrutinized by the other users and is up-voted if it develops similar interests. When the project gathers enough response, it is then made open for public funding. By the use of the database and understanding the priorities of the stakeholders, the companies and their capital has started to come together. Most of the initiatives are proposed through the interface. In many cases, the investments are done by the private stakeholders, this reduces the dependency on the municipality and generates a state of financial self-reliability. The progress of the project can be tracked and users can directly give their feedbacks. As on 2017, the initiatives has kick-started the following projects:

  • Parking space sharing by ParkU
  • Electric bike sharing by Urbee
  • Ubuntu stadtsuin (city garden) by Empowerment co-operative Amsterdam (ECA)
  • E-car charging stations by Gemeente (municipality), Amsterdam
  • Co-working space by Carteblanche (Status-completed)

2)      Madame Mayor, I have an idea!

In Paris, a new participatory budgeting scheme was piloted by the mayor Anne Hidalgo in 2014. Unlike ZO!city, the ideas here were not crowd-sourced, instead the city administration proposed ideas which were then brought upon to the citizens for discussions and voting. In the initial years, the process concentrated on encouraging the citizens to initiate a discussion for the proposals.

After a few years of initiations by the city authority, the participatory budgeting process is now online and fully active (Simon, Bass, Boelman & Mulgan, 2017). The citizens of Paris can now directly propose an initiative by themselves. Currently, the process has five phases distributed (Simon, Bass, Boelman & Mulgan, 2017):

  1. In January and February, the proposals are made online which are supported by many neighborhood workshops.
  2. From March to May, a co-creation process takes place which brings the representatives of similar proposals together.
  3. Over the next few months, the ideas are shared online for public review. Selected by an election committee, these ideas meet the minimum criteria such as, public benefit, technical feasibility, the financial feasibility, etc. During this period, an elected committee assists the people in promoting and campaigning their idea.
  4. In September, the citizens are then allowed to vote for the most desired proposals.
  5. By December, the successful ideas are selected. The implementation and the budget is allocated in the following year.

The progress of the projects can be then tracked through various means such as on online platform, geo-located and overlapped on google maps and by infographics created by the teams.

Response

Since the year of inception, the project has seen substantial growth. Currently, it is considered as one of the biggest citizen engagement programs in practice (Simon, Bass, Boelman & Mulgan, 2017). In 2014, the project received around 41000 votes for various proposals, the number raised to 67000 votes in the next year and then 160000 in 2016. The number of projects selected for implementation also increased from 9 to 219. The transparency of the process, political support and the continuous citizen engagement are the main reasons for the success of this initiative.

Fig 4 – The comparison of the selected projects and the participation of the citizens over the years.

Although, the process of these models are highly inclusive to the context of the neighbourhoods or cities, but still, similar projects in their own capacities have started emerging all over the world. For example, Madrid has ‘Decide Madrid’, which tracks proposals, debates, participatory budgeting and sectoral processes. Jakarta has initiated its share of citizen engagement from ‘Qlue’ which has different interfaces for different activities. Reykjavik, Finland has ‘Better Reykjavik’ working on similar grounds.

KEY TAKE-AWAYS

In the context of a neighborhood, the number of the stakeholders present are never specific and to define the style of interaction among them is rather complex. Even if the decision-making process is spontaneous and time consuming, the objectives can be clearly laid out and a definite process can be put in place. It is equally important to identify the stakeholders and understand their needs and ideas for urban transformation. According to their desires and the ambitions of the stakeholders, a proper network between them should be created. An online interface comes of many uses, a database of the existing stakeholders and their interests can be created. The database can also be created on the basis of the proposals and feedback of the users, at the same time it can also work as a source of information for them. The whole development model is highly transparent and allows the users to track the progress of the projects they are interested in. A possible framework can be summarized in the figure – 5.

Fig 5 – A summary of the possible framework of using an online interface for better citizen participation. (Source – glamourmanifest)

Indian cities has always had diversified actors with multiple interests. Identifying similar interests and developing a network among them can be a complex challenge and also an opportunity. An online interface can help the mediators and the decision makers to derive a sense of order in the complex network of interests and develop an incremental order in the development process.

SCP of Indian cities has considered many initiatives for citizen engagement, for example, Mygov has a forum which actively asks citizens for feedback and discussions, but, a similar model is hard to find. There are many takeaways from the above case-studies that Indian cities can use to develop an effective citizen engagement process. Implementing a similar model can help the decision makers in foreseeing a long-term urban synthesis and develop a sense of trust among the stakeholders.

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Participatory Budgeting

Introduction

Participatory Budgeting is a democratic process in which community members directly decide how to spend part of a public budget. It helps make budget decisions clear and accessible. It gives real power to people who have never before been involved in the political process. (New York City Council, n.d.).

“How do you spend $1 million of the city’s money…?” The pamphlets used in New York’s pilot program on Participatory Budgeting (PBNYC) ask this question to the citizens.

The practice of Participatory Budgeting originated in Porto Alegre, Brazil in 1989. It attracts almost 50,000 citizens every year to deliberate on the utilisation of approximately 20% of city’s monetary resource (Shah, 2007). Its positive impact is a noticeable improvement in the accessibility and quality of various public welfare amenities in those municipalities that have adopted it. The participation and influence of people belonging to low-income groups in the budget allocation process are proof of their empowerment (Bhatnagar, Rathore, Torres, & Kanungo). Numerous governments, NGOs, institutional bodies, social movements and political parties have adopted participatory budgeting to bring changes in public policy and implementation processes.

Participatory Budgeting in India

Since the amendment of 74th Constitutional Act, the interaction of local civic bodies with the decision-making bodies of government ameliorated. Along with this, the sectors of economics, planning, justice and budgeting became transparent to the public and they eventually became crucial stakeholders. A few notable Participatory Budgeting initiatives in India are in Bangalore, Mysore, Pune and Kochi, where formal institutional methods were established which made sure that citizens were also part of decision-making (Shetty, 2015). Bangalore was the first city to implement participatory budgeting and the campaign resulted in citizen’s participation in budget allocation in over 20% of wards in the city (Keruwala, 2013).

Case of Pune

Participatory Budgeting was launched in Pune in 2006 under the then commissioner of Pune Municipal Corporation. Pune Municipal Corporation consists of four zones with 15 administrative wards. Each administrative ward contains 4 to 6 prabhags. Each prabhag (composed of two electoral wards) was allocated a budget of 50 lacs and could execute any number of projects with a maximum cost of Rs. 5 lac per project. For 76 prabhags in PMC, a total of Rs. 38 crore was allocated through participatory budgeting (Keruwala, 2013).

The process begins when Pune municipal Corporation (PMC) invites suggestions from citizens at the respective ward offices. These inputs vary from roads, electricity, buildings to slum improvement and water supply and drainage. Suggestions by the citizens are compiled at the ward office and submitted to prabhag samiti, which in turn sends the approved suggestions for accounts scrutiny to produce a final list of projects to be implemented in PMC region.

Decentralised Planning in Kerala – An Experiment through Ninth Five-Year Plan

In India’s ninth five-year plan, Government of Kerala established a decentralisation plan, which was an outcome of People’s Plan Campaign – an experimental approach to reformations in local planning. Participatory budgeting was first launched in 1996 and covered the entire state including 991 rural villages, 152 block panchayats, 53 municipalities, 14 districts and 5 corporations that represented different levels of administrative bodies (Wilhelmy, 2013).

Following this, in the period 1996 to 2001, the entire state devolved approximately 40% of state revenue into the projects chosen by 65% of the 3 million beneficiary citizens and eventually this model became a part of state planning, now popularly known as Kerala Development Plan (Wilhelmy, 2013). With an objective to ensure that priority projects meet the needs of beneficiary citizens, the model aims to establish civic engagements exercises. Participatory planning in Kerala focuses on local economic development, social justice and various public services with excellence (George & Balan, People’s Participation in Development Planning in Kerala, 2011).

Kerala’s process evolved into a dynamic model with two key features of the campaign – resourceful and trained administration and the extent of involvement of people elected delegates. The model created an inclusive platform of citizens supported by 373 state-level trainers, almost 10,500 trained provincial-level resource persons and 50,000 trained local activists (including 4,000 retired administrators) (Wilhelmy, 2013). The delegates elected by the people were involved in the budgeting process at every phase with a say in raising demands, prioritising projects and development plans (Wilhelmy, 2013).

Stages of Participatory Planning

The procedure of participatory budgeting comprises of six stages:

  • A range of local assemblies/grama sabhas are conducted.
  • Conducting development seminars, which facilitates discussions between politicians, experts and groups of citizens.
  • Preparation of report from the data collected from development seminars.
  • Drafting of project proposals with technical requirements and budget planning details by the ‘task force’ created by the development seminar.
  • Approval of the projects and budget by District Planning Committees.
  • Implementation, monitoring and evaluation of the approved projects.

Public Participation

All stages of participatory planning ensure involvement of the stakeholders. The local government, to execute the initiatives and to promote maximum participation of the public, forms certain Working Groups (Figure 22) that are mandatory in every local body (George & Balan, People’s Participation in Development Planning in Kerala, 2011). These groups include the sectors shown in the diagram. The Working Groups, with an elected head perform effectively to guarantee participation of all marginalised societal groups. At the second stage of participatory planning – Development Seminar, the proposal projects put forward by the Working Group are presented and are subjected to public suggestions and improvements (George & Balan, People’s Participation in Development Planning in Kerala, 2011).

Sectors of Working Group

For maximum participation of all the stakeholders, the respective local bodies ensure communication at all stages from conceptualisation to implementation. Right to Information Act plays a major role in the framework as it facilitates access for public to the processes and documents involved. Kerala Institute of Local Administration (KILA) (George & Balan, People’s Participation in Development Planning in Kerala, 2011) conducts training programmes for members of Ward Sabha/Ward Committee and this capacity building initiative ensures that decision-making process is all-inclusive.

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A Structured Approach to Capacity Building

Evolution of a Strategy

CIDCO is a pioneer in the urban development sector in India and as such, it is constantly evolving in order to be prepared to face the challenges of the modern urban sector. CIDCO recognizes the significance of building its capacity as an urban development agency and it plans to implement many interventions.

For these, CIDCO established the NIUA-CIDCO Smart City Lab, a research and capacity building unit in 2014 through a collaboration with the National Institute of Urban Affairs. Over the last three years, the Smart City Lab has conducted research on smart cities, aided the launch of CIDCO’s Smart City Action Plan and implemented several capacity building interventions for CIDCO. These interventions include a training needs assessment conducted in 2014-2015 and the facilitation of participation in trainings for CIDCO officers.

Recognizing that a lack of clear guidelines and a lengthy approval process for participation in a trainings are a barrier to capacity building, CIDCO adopted a training policy in 2017, with the support of the Smart City Lab.

Subsequently, the Lab developed an online training management system for its implementation. Earlier version of CIDCO@Smart1 has covered both of these in detail. A dedicated training cell at CIDCO maintains the online training management system ‘Ujjwal’. The training policy, Ujjwal and the training cell are the pillars of the comprehensive training programme at CIDCO.

More details are available in the following link:

A Structured Approach to Capacity Building

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Bloom’s Taxonomy

Benjamin Bloom (1913-1999) led a team of cognitive psychologists at the University of Chicago in the development of a framework to categorize learning objectives. This framework was published in 1956 and is known as Bloom’s Taxonomy. It is a method of organizing learning goals and objectives. Bloom’s Taxonomy provides an excellent structure for planning, designing, assessing and evaluating training and learning effectiveness.

One of the major tasks in the process of designing a course is to define the expected learning outcomes or goals. Bloom’s Taxonomy helps to provide a standard language about learning goals and objectives for this. The model uses three domains to classify learning objectives of a course:

  • Cognitive Domain (Intellectual Capability, i.e. Knowledge, or ‘Think’)
  • Affective Domain (Feelings, Emotions and Behaviour, i.e. Attitude, or ‘Feel’)
  • Psychomotor Domain (Manual and Physical Skills, i.e. Skills, or ‘Do’)

‘At-a-glance’ representation of Bloom’s Taxonomy

Each domain is further broken up into tiers. The three domains have been structured in a hierarchy – first Cognitive, then Affective and finally Psychomotor. Each domain must be mastered before progressing to the next. Cognitive Domain focuses on knowledge, Affective Domain focuses on the attitude of the participants while the Psychomotor Domain covers development of physical and bodily skill. – Affective Domain should arguably cover all levels of each domain, particularly in organisations seeking learning at an institutional level.

The work done by Bloom and his team primarily focused on the Cognitive Domain, breaking it down into six categories or tiers as shown in the image. Each of these six tiers also reflect the degree of difficulty of the participants, starting with Remember and increasing in level all the way up to Create.

Structure of the Cognitive Domain

The learning goals in the design of a course for the participants can be specified using this tool. Most learning interventions tend to focus on Remember, Understand and Apply. Analyse, Evaluate, and Create are more relevant when the learning objective is to ‘break down information into parts the learning is being applied to real life situations. These six categories were initially defined as Knowledge, Comprehension, Application, Evaluation and Synthesis.

Bloom’s Taxonomy has two main applications:

  • As discussed earlier, it is directly useful in planning, designing courses and their effectiveness.
  • It can be used as a checklist to ensure achievement of learning goals for participants in a course by testing the validity and coverage of the concepts covered.
  • It is also used to quantify and compare level of assessment.

Bloom’s taxonomy is considered relevant in all types of learning, including workplace learning. The objective of workplace learning is for learners to not only remember and recall facts and procedures but to also be able to apply their learning to authentic workplace situation to improve on the job performance. For CIDCO, bridging the gap between knowledge gained by its Officers and its application in their everyday work is essential for effective learning. As CIDCO Officers continue to participate in trainings through Ujjwal, the Training Cell aims to ensure that they master the principles of the each of cognitive learning category before progressing on to the next. Moving from ‘remember’ to ‘create’, they will eventually, integrate advanced and creative out-of-the-box thinking in their work, with an emphasis on the formulation of new patterns and structures.

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An Alternative TOD Policy

Delhi, as the centre of the National Capital Region (NCR), needs to reaffirm its status as the primate city in the central NCR. In spite of a declining population growth, Delhi has sprawled to an area double its size over the last decade. This is the consequence of an auto-centric bias and a shortage of affordable housing. People are living further away from places of their work and spending more time on their daily commute on road. Delhi’s adoption of TOD as a strategy will help rein in this sprawl and improve the quality of life.

Recognising this, UTTIPEC (DDA) initiated the development of a TOD Policy for Delhi in 2009. In 2012, the first draft of the policy was completed. A revised version of the policy approved by the MoUHA in 2015. The policy was further modified and published for comments in 2016. The notification of the Policy in the Gazette of India in April 2016 showed a dilution of the progressive standards set in the 2012 Draft. It also the showed the difficulty of changing the behaviour of an auto-centric city. This is in spite of the large scale capital investments in public transit made over previous decade. NIUA-CIDCO Smart City Lab was invited to share comments on Delhi’s TOD Policy earlier in 2017. As an additional exercise, the Lab also prepared a draft alternative TOD Policy for Delhi.

This draft borrows from findings of a study conducted by NIUA on Transit Oriented Development in Indian Smart Cities. It suggests for a focus on the following:

  1. Need for the policy – The policy should present a clear status of Delhi’s infrastructure, making the case for the TOD policy. It should focus on NMT, public transit, housing infrastructure and upcoming transport investments. The policy should focus upon the principles of TOD and using the five constructs outlined in NIUA’s TOD study in 2016-17.
  2. Policy statement – It should highlight the significance of the TOD Policy in managing Delhi’s growth. It should also clearly state the policy’s intention of maximising sustainable mobility and development practices in Delhi.
  3. Existing legal provisions relevant to the policy – The policy must recognise the legal framework already in place that supports implementation of a TOD. It should highlight the provisions within Master Plan for Delhi 2021 and the National Urban Transportation Policy 2014 that enable the implementation of TOD.
  4. Applicability of the policy – The policy should clearly identify the areas within Delhi for its application. It should also enumerate the various public transit stations and nodes in the city that can be developed as a TOD node.
  5. Exclusions to the policy – It should identify the areas within the city where the policy cannot be applied. This should be with respect to the presence of historical structures and other conditions protecting their status.
  6. Guidelines for implementation of the policy – The tools useful in the implementation of a TOD should be discussed within the policy along with the guidance for their use. a. Instruments of a TOD, namely Value Capture Finance, Land Pooling and Joint Ventures. b. TOD Project types based on Influence area development and public transit type c. Differences between a Greenfield and Brownfield development within a TOD.
  7. Key Highlights of DCR – Finally, the policy should present the modifications in DCRs necessary for implementation of TOD. This should cover standards for density, FSI, road design, car parking, land use mix and universal access.

TOD implementation in a city requires adoption of its principles through an incremental approach. Given that this process stretches over years, it requires a clear guiding framework. The policy offers this framework by integrating existing statutory documents and regulations. It attempts to shift the focus from the solutions to the mechanisms of their delivery. By doing so, this alternative draft TOD Policy for Delhi aims to overcome institutional barriers to the success of a TOD. Recommendations of the policy focus on a incremental approach that allows the city to transform neighbourhoods one step at a time with simple interventions. Highlights of the recommendations made in the policy draft are:

  1. Implement TOD around existing public transit stations, using them as nodes.
  2. Prioritize the TOD implementation around multi-modal hubs (metro stations, interchanges, railway stations, bus terminals and airport terminals).
  3. Maximizing access to these transit stations by developing bicycle-pedestrian infrastructure, strengthening existing IPT with the ‘influence area’.
  4. Limit parking within 100 m of these stations.
  5. Ensure convenient transfer between different modes of public transit by implementing seamless integration.
  6. Focus on achieving a high density of jobs and households, with a minimum density of 175 inhabitants per hectare.
  7. In case of the implementing TOD on MRTS (metro), High densities centred at the stations will automatically form a contiguous band of Influence Zone or Corridor since the average distance between the stations is less than a km.
  8. Investments in the improvement of influence area improve value of the neighbouring property. Adopt VCF policy to capture some of this financial increment.
  9. Use mechanisms such as a Business Improvement District (BID) at District Centres in Delhi to finance physical improvements for pedestrian and NMT infrastructure and open space in the influence area.
  10. Use PPP or Joint Development models for financing TOD and engaging with the private stakeholders.
  11. Revise the DCR to enable implementation of all these interventions.
  12. Scale all interventions based on the extent to existing development. The various types of development recommended are as follows: Brownfield – Retrofit; Brownfield – Infill; Brownfield – Redevelopment (New Development); Greenfield (New Development).

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Smart City Plan: Columbus, Ohio

US DOT’s Smart City Challenge aims to help cities begin to address the challenges the trends identified in the Beyond Traffic report published by the U.S. Department of Transportation (U.S. Department of Transportation, n.d.). As part of the challenge, 78 medium-sized cities shared their best and most creative ideas for innovatively addressing the challenges they face. USDOT committed $40 million for one city to demonstrate how advanced data and intelligent transportation systems (ITS) technologies and applications can be used to reduce congestion, keep travellers safe, protect the environment, respond to climate change, connect underserved communities, and support economic vitality (U.S. Department of Transportation, n.d.). In June 2016, Columbus was selected as the winner of the Smart City Challenge. Columbus proposed to reshape its transportation system to become part of a fully integrated city that leverages data and technology with an aim of efficiently moving people and good (U.S. Department of Transportation, n.d.). The 77 cities that did not win the Smart City Challenge benefited from the challenge as well, as the competition gave them an opportunity for creation of detailed applications that spurred additional interest in smart city technology with respect to the challenges the cities are facing (Maddox, 2016)

The Winner – Columbus, Ohio
Ohio’s capital Columbus is the largest city in the state and the 15th largest city in the country with a population of 8,60,090 (US Census, 2016). It is relatively dense for a mid-sized American city. It has a density of about 3800 inhabitants per square mile (around 1500 persons per square km). It serves as a strong regional anchor with 39% of the Metropolitan Area population living in the city. Columbus has grown consistently over time and it continues to become more diverse with growing African-American, Latino and Asian populations. The city has a young working population with a median age of 32 years which is lower than the state (39) and the nation (37) (Bureau of Labor Statistics, 2017) and has a lower unemployment rate of 3.4% (U.S. Census, 2015). Columbus has a strong and diverse economy, driven by education, healthcare and social assistance services. It is also the fastest growing metro area in the Midwest, the top metro for job growth in the Midwest, and the top metro for wage growth in the U.S. The city recognises these credentials and aims to leverage them to make the City of Columbus- A city of opportunities (Ginther, 2016).

“Columbus won the Smart City Challenge because of Mayor Ginther’s leadership and because the central Ohio community united to develop innovative solutions to address community challenges.” – Sherrod Brown (D-OH)

Skyline of Columbus (Source: https://www.columbus.gov)

Columbus Smart City Vision
With its immense potential and resources, Columbus is striving to become a successful smart city by responding to four primary issues:

  • An aging population;
  • A growing younger population that is moving to the dense urban areas;
  • Mobility challenges in select neighbourhoods; and
  • A growing economy and population with related housing and commercial, passenger and freight, and environmental issues.

Its vision is to become a community that provides beauty, prosperity and health for all of its citizens (Ginther, 2016). It plans to achieve its vision by:

  1. Leveraging a new central connected traffic signal and integrated transportation data system to develop a suite of applications to deliver enhanced human services to residents and visitors.
  2. Integrating electronic appointments and scheduling platform for doctor visits with transit tracking so that rescheduling becomes automated and expecting mothers do not gave to wait weeks to reschedule appointments. These applications include a multi-modal trip planning application, a common payment system for all transportation modes, a smartphone application for assistance to persons with disabilities, and integration of travel options at key locations for visitors.
  3. Establishing a smart corridor connecting underserved neighbourhoods to jobs and services. The smart corridor will enhance Bus Rapid Transit (BRT) service by installing smart traffic signals, smart street lighting, traveller information and payment kiosks, and free public Wi-Fi along the route. Further, six electric, accessible, autonomous vehicles will be deployed to expand the reach of the BRT system to additional retail and employment centres (U.S. Department of Transportation, n.d.).

Smart City plan for Columbus adopts Transit Oriented Development (TOD) as an approach for managing city’s transportation. Mayor Andrew J. Ginther understands the connection between city’s transportation and the livelihood of the people. He believes that “Transportation is not just about roads, transit and ride sharing. It is about how people access opportunity. And how they live”. (Smart Columbus, n.d.) Globally, many cities are appreciating and adopting TOD approach to build more liveable cities. In fact, after focused efforts to dovetail infrastructure and technology through its AMRUT and Smart Cities programs, the Government of India is now turning its attention Transit-Oriented Development  (TOD). It has also recently adopted a national TOD policy that will support the transformation process already underway in most of the Indian cities. This transformation will attract lot of  investments to the respective cities, and vastly increase their ‘liveability’ in a sustainable manner.

Highlights of the proposal
There are four foundational plans, which will allow the city to identify and overcome the challenges for achieving its desired goals (Ginther, 2016).

  1. Connect Columbus: Connect Columbus is the City’s Multimodal Thoroughfare Plan which provides a long-range vision and priority investments for transportation plan in the City. The plan aims designed to improve safety, reduce congestion, assist children, the elderly, and people with ADA needs and promote economic development, fitness and environmental responsibility.
  2. Insight 2050: Mid-Ohio Regional Planning Commission (MORPC), the metropolitan planning organisation for Columbus, leads Insight 2050. It is a collaborative initiative among public and private partners designed to help Central Ohio proactively plan for growth and development. Insight 2050 provides scenario testing tools and data to help decision makers understand the impact of future land use policies and the transportation investments.
  3. 2016-2040 Metropolitan Transportation Plan: As the region continues to grow and funding availability becomes scarce, the region is prepared with innovative transportation solutions to address grown infrastructure demand. The Metropolitan Transportation Plan is the federally mandated long-range planning document led   by MORPC that brings together local governments from around Central Ohio and other local, state, and federal agencies to identify and coordinate transportation goals, policies, strategies and projects over the next two decades.
  4. NextGen Plan: The NextGen Plan is the Central Ohio Transit Authority’s (COTA) long-range planning effort to identify transit needs and opportunities for 2025, 2040 and 2050. The initiative will recommend system enhancements, including a prioritised list of bus and rail projects along with what technology to employ. COTA will comprehensively realign its network to better fulfil the needs of the growing community.

Columbus has outperformed a number of other deserving cities, which were far more technologically advanced and financially stronger, because:

  1. The proposal provided a path for growth beyond the initial applications through its clearly defined vision and goals (McGregor, 2016).
  2. Its focus on improving the health and lives of the community by reducing poverty and infant mortality with the application of technology (Hawkins, 2016).
  3. The ability of the city to rope in local partners as well as prominent tech-based companies to help in achieving the goals set for smarter Columbus (Chieppo, 2016).

Key points of comparison with Indian Smart Cities
The greatest difference between cities participating in the Smart City Challenge in the United States and those participating in the Indian Smart Cities Mission is the level of existing infrastructure. Columbus additionally illustrates a strong commitment to the sharing economy and has a foundation for providing open, accessible data that enables other stakeholders to develop solutions for the greater good. This is also evident from the city’s investment in policy and regulatory changes that encourage bike sharing (CoGo) and car sharing (Car2Go, Uber) services. The city also has a working MyColumbus app that enables citizens to access (Ginther, 2016):

  • City services
  • Publicly accessible transit routes, schedules, and stop data
  • MORPC Regional Data Lab portal that provides access to transportation, housing, and other public information available around the region
  • State-wide accessible travel-time data Indian cities, on the other hand, have taken up a bigger challenge of leap-frogging development. As seen through the new urban agenda and its initiatives, Indian Smart Cities are bridging the existing service delivery gaps while embedding “smartness” into the system in the process.

Columbus’ Smart City Plan also successfully leverages about 10 times the initial government grant by building partnership with the private sector. A review of the finances from the first 33 cities shows an average funding leverage of 1.18, with a maximum of 5.29 in case of Indore and a value less than 1 for more than half the cities. However, with the growing focus on engaging with private partners (as seen under the smart cities mission) and the adoption of the country’s first value capture finance policy framework in February 2017, Indian cities are now set to find more opportunities for leveraging finances from alternative sources.

Some of the Indian cities are already demonstrating steps in this direction through the implementation of global best practices. An example of this is the city of Pune. Under the Smart Cities Mission, it is collaborating with Google, L&T and other technology firms to provide Wi-Fi connectivity at around 200 strategic locations in the city (Press Trust of India, 2017). Under the contract, Google will help monetise the city Wi-Fi network, and will deploy Google Station platform, which has Wi-Fi network management capability, and focuses on monetisation to make Wi-Fi self-sustainable. RailTel, on the other hand, will provide lat-mile fibre connectivity on need basis to enable Wi-Fi hotspots at around 200 strategic locations across the city (Khan, 2017).

With the support of national level initiatives such as the Smart City Mission, AMRUT among others, cities are working towards efficient and fast project through a collaboration of urban local bodies, state agencies, and local partners including NGOs, educational institutions and community. As Ohio implements its Smart City Plan, there is an opportunity to observe and benefit from the challenges they face and to aid leap-frogging the development in Indian cities.

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The Art of Learning Design

‘Employees are key players contributing to the core competencies of the organisation’ –
Hamel and Prahald, 1994

Introduction to Instructional Design

For the management of any organisation, the objective of providing training to its employees is to bring about a change and development that makes them more efficient at their job. Elaborate planning and arrangement of instruction is important for ensuring quality in this education. Instructional design leads the way in accomplishing this goal through better, more effective teaching (Göksu, Özcan, Çakir, & Göktas, 2017).

Instructional design can be defined as a systematic method that (a) covers such stages of the teaching process as analysis, design, development, evaluation, and management; (b) is based on instructional and learning theories; and (c) enhances the uality of teaching (Göksu, Özcan, Çakir, & Göktas, 2017; Dick, Carey, & Carey, 2001; Dooley, 2005). It aims for a learner centered approach rather than the traditional teacher-centered approach to instruction for effective learning. This means that every component of the instruction is governed by the learning outcomes, which have been determined after a thorough analysis of the audience/learners’ needs (McGriff, 2000).

Most instructional design models are built upon the ADDIE model created by the Center for Educational Technology at Florida State University for the U.S. Army (Göksu, Özcan, Çakir, & Göktas, 2017; Branson, et al., 1975; Dooley, 2005; Hoogveld, Paas, Jochems, & Merriënboer, 2002; Zheng & Smaldino, 2003). ADDIE stands for Analysis, Design, Development, Implementation and Evaluation. It is a cyclical model used in design and delivery of trainings/learning modules. ADDIE is linear in nature and as a result its implementation can become comparatively lengthy and costly, however, its dynamic and flexible nature makes it a popular approac for development of instructional material (Welty, 2007). It is often employed for compliance training and other learning events that are not time sensitive. However, there are modified versions of ADDIE that are more dynamic and interactive. Rapid prototyping (continual feedback) has sometimes been cited as a way to improve the generic ADDIE model.

The ADDIE Model

The different phases of the ADDIE process—Analysis, Design, Development, Implementation, and Evaluation—provide a roadmap for the entire instructional design process.

Analysis: This stage includes identification of the learning problems, goals, objectives, participant’s needs and their existing knowledge and skills. It is done taking into account the learning environment, delivery options, project timeline and any other relevant constraints. The output of Analysis phase is the input for the Design phase.

Design: The primary goal of this stage is to translate the goals of the course defined in the Analysis phase into performance outcomes and course objectives. This includes specification of learning objectives, development of detailed storyboards, prototypes, content and presentation methods.

Development: The purpose of this phase is to generate lesson plans and lesson materials. This includes the actual creation (production) of the content and based on the output of the Design phase (McGriff, 2000). It involves preparation of draft material and activities, their testing and revision and preparation of training material as per requirement.

Implementation: This is the phase where the actual delivery of the training takes place through a given medium. Purpose of this phase is the effective and efficient delivery of instruction. This phase must promote the audiences’ understanding of material, support their mastery of objectives, ensuring transfer of knowledge from the instructional setting to the job (McGriff, 2000).

Evaluation: This is the phase where the effectiveness and efficiency of the instruction is measured. Evaluation can be of two types: Formative and Summative. Formative Evaluation is conducted during and in between the different phases of the ADDIE model. Its purpose is to improve the learning/training module before the final version is delivered. Summative Evaluation occurs after the final version of learning/training module is delivered. It assesses the overall effectiveness of the module. Data from this phase is used to make decision about the module (McGriff, 2000).

Over years, practitioners have developed customised versions to suit the specific trainings/learning needs. This includes PADDIE + M, where the P stands for planning and M stands for Maintenance, and ADDIE+M, where Μ stands for Maintenance of the Learning Community Network after the end of a course. Today, the influence of the ADDIE can be seen on most Instructional Design models being used. ADDIE’s success is a result of its flexibility, simplicity and efficiency. Its iterative nature allows consistency within the model and opportunity for improvement until the learning needs are met, ensuring a robust training/learning module.

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Data and Decision Making for Transportation

India’s transport sector is large and diverse, it caters to the transport needs of 1.1 billion people (IIHS, 2015). The absence of a database with scientific management and analysis of urban transport statistics has severely constrained the ability to formulate sound urban transport plans and reliably assess the impact of the different projects carried out in the cities (IIHS, 2015; Ahluwalia, 2011; Agarwal, 2006).

As Indian cities implement information technology services (ITS) to improve transportation planning and operations in urban areas through programmes such as the national Smart City Mission, there is a opportunity to address the following:

  • Establishing standard for data collection and management across various transportation systems
  • Standardised automatic data collection systems across transit systems in conjunction with ITS
  • Coordination and integration of data collected by multiple agencies and in multiple formats
  • Maintenance of regular up to date data for larger policy and planning functions
  • Open data for the research community and to drive innovation in tech solutions
  • Building a legal framework to guide data collection and sharing
  • Protection of transit users’ privacy

Automated Data Collection System is an IT based data collection system that can be used to gather data about transportation services and facilities. Its key components are (Wilson, 2011):

  1. Automatic Vehicle Location System (AVL)
  2. Automatic Passenger Counting Systems (APC)
  3. Automatic Fare Collection System (AFC)

ADCS are important for collecting big data, as the use of technology enables data collection at a high speed and large volume. Such data can be used for (TfL,2014):

  • Asset Maintenance
  • Road Traffic Management
  • Informing users’ decisions
  • Management of public transport services

Cities across the world already implement the system at different scales and in different operations to make the system more efficient. Notably, Transport for London (TfL) uses big data from ADCS to manage its road traffic and parking management.

Parking and Data
Data collected through for parking through the use of IT tools helps with the following (Gowd, 2015)

Efficiency Management

  • Big data can help predict capacity patterns, enabling deployment of appropriate resources.
  • Data on capacity patterns allow the city to adjust rate structures and maximum time stays which benefits both motorists and retailers/businesses

Revenue Management

  • Using revenue trends and variations in revenue cycles to program variables like maximum parking time, rates, and enforcement hour
  • Occupancy trend versus paid parking spaces to help the city increase its revenue

Parking Metre Management

  • Real-time metre status and faults, in combination with data on past trends can help metre maintenance personnel mitigate device failure risks, thus reducing impact to capacity and revenue
  • Collecting and analysing user key strokes can help a city to understand metre user interface navigation patterns while power consumption data based on location can reduce failures.

Smart Parking for London Underground – TfL

In order to better  understand the parking use of London Underground’s 61 car parks (with about 10,000 spaces), TfL introduced smart parking technology to provide real-time information accessible through smartphones and satnav devices, allowing commuters to better plan their journeys and make informed choices about how, where and when they travel (Smart Parking Ltd).

  • This involved use of SmartEye – a vehicle detection sensor connected to SmartRep – a parking management software using SmartLink data transmitters across 28 of the car parks.
  • TfL then shares the occupancy data collected through the sensors through a dynamic feed, informing the public about the availability of parking spaces.
  • Smart Parking data is available free of charge at http://tfl.gov.uk/developers and is used by nearly 500 third party apps, helping visitors to plan their travel (Smart Parking Ltd).

Traffic Management and Data

Traffic management is the planning, monitoring and control or influencing of traffic. It aims to:

  • maximise the effectiveness of the use of existing infrastructure;
  • ensure reliable and safe operation of transport;
  • address environmental goals; and
  • ensure fair allocation of infrastructure space (road space, rail slots, etc.) among competing users

The solutions for managing traffic can include:

  • Traffic Signal Monitoring and Management System: for real time measurement, analysis and adjustment of the signal to improve traffic flow
  • Fixed Sensors such as CCTV/Traffic Cameras to monitor traffic, particularly to track congestion and traffic. They include loop detectors (detecting vehicles passing a certain point – such as a traffic signal)
  • Mobile Sensors such as GPS/Mobile phone/Dashboard Camera to collect Floating Vehicle Data(FVD), which can be used to determine speed, location and direction of travel. Crowd sourced data from social networking sites is also useful, particularly in case of accidents or other emergencies.
  • Freeway electronic message signs for information dissemination to the users (for speed management, ramp metering and tactical management of traffic)

Urban Traffic Management in UK

The Highways Agency (HA) uses road sensors to collect data on traffic flows and GPS data to estimate journey time. It has been using techniques such as self-regulating co-ordinated traffic lights, traffic cameras and variable-message signs to reduce traffic delay and congestion for decades. Now, it has begun to use big data in order to gain insights into traffic patterns. Its National Traffic Information Service collects data and provides real-time traffic information through media channels and through the data.gov.uk website. Private companies, such as Inrix and TomTom, also use the data collected through vehicle fleets to gather information on traffic flows and delays.

JamCams

Transport for London (TfL) also uses a “JamCam” system to provide nearly live video clips from all existing cameras. The videos give a better indication of the actual traffic flow compared to static images. Each clip is five seconds long, and encoded using H.264 at the same resolution as the static JamCam images.

Both the examples illustrate use of the data for:

  • Immediate information sharing for the users of the system such availability of parking
  • Efficient processes and ease of accessibility, for example online payment of parking fees, reservation of parking space online, etc.
  •  Long term planning and decision making for the administrators based on analysis of trends, for example prediction of parking requirements based on use trends

Initiatives @ CIDCO

CIDCO is also implementing Traffic Management System and Smart Parking as part of CIDCO Smart City (South). The Traffic Management System includes – Area Traffic Control, which will include real time traffic monitoring, traffic surveillance, synchronised signalling and loop detection. Smart Parking will be implemented at 17 locations. It will include sensor based occupancy detection and real time information dissemination about parking availability through website and mobile based applications. Parking payment will also be managed through these medium.

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