Jakarta's metropolitan area, with a population of 28 million, is one of the few major world cities with no rapid transit system. Its public bus system carries just 400,000 people a day, and the peak hour dedicated bus lane is freely ignored by cars, motorcycles and even official vehicles.
It is no surprise, then, that the capital's streets are hit every workday by almost 10 million cars, motorcycles, trucks and other vehicles, according to the Jakarta Transportation Agency. Nearly two million of these are driving in from neighboring municipalities in the provinces of West Java and Banten.
Jakarta was named the world city with the worst traffic in one index based on satellite navigation data, which found the average driver starting and stopping more than 33,000 times in a year. An estimated 70 percent of the city's air pollution comes from vehicles.
The authorities have recognized that their transportation nightmares are a serious threat to the functioning of the city, and have a target to increase the share of trips on public transport from 23 percent to 60 percent by 2030.
However, private automobile traffic is not going away any time soon, and one challenge faced by city administrations is how to manage it and how to integrate it into a comprehensive urban transit system.
To achieve this aim will require the integration of public and private transport data, leading to aggregated, comprehensive and real-time data on road traffic. Data analytics is clearly key to resolving Jakarta's transport woes; how can the science be applied?
A project at MIT's Department of Urban Studies and Planning points the way. A number of papers published under this project show the need for every element of the public transport eco-system to be interconnected, to provide critical real-time data. Analysis of this data can serve to augment intelligence and manage anomalies in real time.
Predictive maintenance, for example, can be scheduled to minimize vehicle breakdowns, the great bugbear of commuters. Data feeds on the areas and timings of regular traffic congestion can allow for the planning of more efficient bus routes as well as managing peak-period congestion at bus stops with more frequent services for popular routes.
This may sound esoteric but it is really not rocket science, and other countries are already using data analysis to help manage their public transport issues. What can we here in Singapore learn from best practices around the world that are alleviating these challenges for transport authorities, service providers and consumers?
According to a report by McKinsey & Company, collection and strategic use of information can improve forecasting and help to nudge behavior in ways that improve the reliability of transport infrastructure and increase its efficiency and utilization. The report cites as an example the fact that Israel has introduced a 21-kilometer fast lane on Highway 1 between Tel Aviv and Ben Gurion Airport.
The lane uses a toll system that calculates fees based on traffic at the time of travel. To make it work, the system counts the cars on the road; it can also evaluate the space between cars to measure congestion. This is real-time pattern recognition of a very high order. The information is then put to use in a way that increases "throughput," or the amount of traffic the road can bear. If traffic density is high, tolls are high; if there are few cars on the road, charges are cheap. This not only keeps toll revenues flowing but also reduces congestion by "steering" demand.
Holland, too, is benefiting from the application of big data analysis. Dutch Railways is the principal passenger railway operator in the Netherlands, providing rail services on the Dutch main-rail network and international services to other European destinations. Running these vast networks gives Dutch Railways access to huge amounts of data, collected through intelligent train technology, ticketing systems, travel information real-time monitoring and services for maintenance and control unit staff.
Until now, train suppliers delivered all this IT, so each type of train had its own IT environment – making it difficult to work together and maintain each system. Dutch Railways had a vision to integrate all these information to deliver more reliable and better serves to customers.
Using streaming analytics, in-memory computing, integration and messaging software, Dutch Railways is now able to provide real-time information about train services and maintenance scheduling. Commuters are also able to use a travel planner application to ensure a seamless and prompt commute.
The clear conclusion is that digitizing infrastructure networks can improve forecasting, promote reliability and increase efficiency.
So what are the next steps?
The Jakarta authorities have already taken the first step with the commitment to the MRT. The challenge now is to open up and encourage the sharing of transport data among all the stakeholders – transport operators, system providers and citizens alike. In our opinion, this will speed up the development of practical solutions to reduce congestion, improve waiting times and overcome commuter inconvenience. Embracing technology in this area will not only improve our daily lives but provide important support for Jakarta's role as a leading regional city.
Erich Gerber is a general manager for Asia Pacific and Japan at TIBCO Software.