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 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 website. Private companies, such as Inrix and TomTom, also use the data collected through vehicle fleets to gather information on traffic flows and delays.


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.