This is the second of a series of articles on how data can be used to fuel economic development. In this article, we explore the role of data in improving transportation services. The need for transportation arises when different land uses like residential, industrial and commercial are too far apart. For example, many people live far from their work places. Similarly, not all the goods, services or amenities one needs can be readily found in aneighbourhood. Transportation fills this gap by bringing people closer to opportunity.

Smart data collection can also help ease traffic congestion. First step towards relieving the congestion is determining the speed at which vehicles move on roadways during various times of day

However, transportation as we know it is inadequate to meet the challenges of today’s needs. Efficiency and effectiveness of our transportation services will have to be dramatically improved, particularly in growing metropolitan areas like the planned Colombo megalopolis, if we are to ensure liveable, healthy and sustainable urban environments. Data holds the key to achieving a reliable transportation service in such complex urban environments.

We ought to begin this discussion with a simple example. How easy is it for a person strange to Colombo to find his way around and decide the combination of bus routes he should use to get to the intended destination? Not easy is probably the answer. Planning one’s journey in a metropolitan area should be a painless exercise,  particularly  given almost everyone holds a smartphone in their hands today. Fortunately, there are a few applications surfacing now enabling one to plan their journey in Colombo using public transit. A major limitation in these apps, however, is the incompleteness of data. It is time that Sri Lanka joins the many transport authorities around the world who are capturing and publicly releasing comprehensive datasets about their public transportation schedules and associated spatial information.

However, it is imperative to use standard data formats when doing so. One such useful format is the Google Transit Feed Specification (GTFS). In addition to routes and timetables,  the GTFS also supports the inclusion of fares and fare zones which are very relevant information for a passenger. Next element missing in available apps is the support for multi-modal trip planning. If properly designed, these apps can provide much needed data to identify how people use the public transportation in a given area, particularly in the absence of comprehensive travel surveys.

Also to this end, introduction of smart ticketing system would be beneficial, at least in the planned Western Province megalopolis area to begin with. In addition to direct benefits to passengers, like the ability to use one card in buses and trains, these systems generate a stream of data about trip origins and destinations that can be used to help better planning of services.

Naturally, the first question that comes to a person standing at a busstop is ‘how long do I have to wait for the next bus to come?’ Unfortunately, this is a question that cannot be reliably answered in a city where congestion reigns. However, technology that becomes cheaper by the day helps us answer a slightly different, nonetheless, equally useful question which is: ‘how far is the next bus servicing this route from me?’ Research shows that the willingness of passengers to use public transportation is high when they can access real-time location information. The technology that enables us to achieve this is called the GPS vehicle tracking. In addition to passengers, real time tracking is obviously useful for bus and train operators.

Law enforcement agencies will also find this information useful to keep speeding drivers at bay, thus reducing accidents. Real time tracking, coupled with bus only lanes and traffic signal prioritisation during peak hours could provide enough incentive for commuters to switch from cars to public transit.

Smart data collection can also help ease traffic congestion. First step towards relieving the congestion is determining the speed at which vehicles move on roadways during various times of day. Burying inductive loop detectors has been the traditional choice, but there are a number of new technologies that can complement those buried detectors. Live data from GPS enabled devices and more recently Bluetooth and WiFi enabled handheld devices and vehicles are gaining traction as means to detect traffic speeds. Live traffic data is used by various navigation apps to provide dynamic rerouting, thus allowing motorists to avoid congested road segments.

Rough roads full of potholes and craters can only add to the misery of motorists who are fed up with traffic jams in cities. Every year individual motorists incur significant repair costs due to rough roads. Bad roads cost businesses hundreds of thousand rupees in repair, wasted fuel and lost time. Moreover, it has been estimated that failure to spend a certain amount in road repair will typically result in seven times that cost five years later. To act swiftly, continuous monitoring of road surfaces is required.

This has been a costly and time consuming task in the past, but not anymore thanks to modern smart devices. Crowdsourcing road surface condition data is now possible, and already there are apps that harness smart devices’ accelerometer to detect potholes.

Similarly, there are some other apps that allow public to take geotagged photos of potholes and send them to authorities via text, Twitter or Facebook. Further analysis of large volumes of crowdsourced data can help detect frequently damaged road segments, thus pointing to underlying causes such as poor drainage that warrant separate attention.
In fact, transport authorities should actively listen to what road users have to say about the entire transport experience. This firsthand information is undoubtedly gold dust for improving transport services. Thanks to the popularity of social media and penetration of smart devices, listening to public has never been easier. At times, social media can indicate service breakdowns even before the network operators become awareof the unfolding situation through other means. There are technologies and analytical methods already in existence that can fetch real time social media feeds and perform analyses to identify such incidents. It only requires a clear vision and a genuine effort from the part of policy makers and administrators to make the best use of existing resources.

(The writer is a Research Group Leader at the SMART Infrastructure Facility, University of Wollongong, Australia.)