Data-jam: Could data reduce road congestion in Dhaka?

  • Blog Post Date 01 February, 2016
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Filippo Sebastio

Business for Social Responsibility


While urbanisation is key to economic growth, failure to address the downsides of the process - such as congestion - may deter the ability of cities to achieve their full growth potential. This column examines the challenges of road congestion in Dhaka, and explores the potential for traffic data to uncover evidenced-based policy designs that can effectively mitigate the problem.

Developing countries are urbanising at impressive rates with trends set to continue. Urbanisation is an instrumental stepping stone for countries moving from poverty to prosperity. In fact, the relationship between urbanisation and income is stronger today than it was in 1960. Figure 1 illustrates that, on average, higher levels of urbanisation in 1960 are associated with faster growth rates. This might reflect the positive impact that higher density has on productivity and employment (Dempster and Glaeser 2015).

Figure 1. Urbanisation and economic growth rates

Urban congestion

Despite the potential to drive growth, one cannot ignore the considerable downsides that come with increased density, including congestion, public health and environmental challenges, and insufficient affordable housing stocks. In the recent decades, Dhaka has faced all of these challenges.

Tackling these downsides requires effective urban governance with a better understanding of the scope and magnitude of the gaps and limitations of existing infrastructure and public service delivery.

Economic density of Bangladesh’s urban areas (GDP (gross domestic product) or value-added per square km) is low in comparison to global averages. One of the reasons for the economic underperformance is the urban congestion which constrains local productivity.

According to the Bangladesh Road and Transport Authority (BRTA), every year around 37,000 cars are added to Dhaka’s roads, of which 80% are private cars. The number of private cars is likely to grow further given that currently only 10% of Dhaka’s commuters own one. The road network in Dhaka is nearly 3,000 km with 200 km primary, 110 km secondary, 50 km feeder, 2,640 km narrow roads, and a few alternative connector roads. The proportion of road surface to built-up area is approximately 7%, much lower than the 25% recommended for good city planning (Mahmud and Rabbani 2012). There are more than 100 open street markets and 3,000 shopping malls, all built alongside roads without adequate parking provisions. Policies are limited and enforcement is even weaker.

Congestion is not an exclusively developing-country problem – London and New York have traffic jams just as severe as those in Delhi and Lagos. Without proper planning, congestion worsens as incomes rise, as increasing proportions of the population begin affording vehicles.

At a minimum, an effective public sector should invest in and maintain a basic system of public transportation infrastructure and implicitly (or explicitly) (Glaeser and Sims 2015) establish usage rules and pricing. This requires balancing accessibility and sustainability of transportation.

Data and policy: A promising partners

Collecting the right data can allow for analysis and shed light on the causes of congestion in Dhaka, and potentially spearhead an open evidence-based discussion on possible solutions. Daily data on traffic can be merged with socioeconomic and event data (strikes, floods etc.) to pattern travel-mode choices and generate evidence on the potential causes of traffic jams. This evidence can be used to tailor policy interventions and assess implementation feasibility.

The figures below, created for a collaborative project between the International Growth Centre (IGC) and BRTA, serve as an example of the kinds of congestion data that can be collected and analysed as evidence of the gaps within existing transport infrastructure. The map in Figure 2 identifies the location of the RFDI towers in Dhaka – traffic data collector sensors that count the number of vehicles – and illustrates traffic volumes observed on 1 January 2014. The most trafficked areas are New Airport Road, Mohakhali and Farmgate - the main arteries of the city.

Figure 2. Traffic densities around Dhaka, 1 January 2014

Note: The diameter of the circles correspond to traffic volumes.

Hourly traffic volume data in Figure 3 below provides a more granular analysis of traffic flows on the same day. The volume peaks at the two main commuting times, 9 am and 6 pm.

Figure 3. Hourly traffic volumes in Dhaka, 1 January 2014

Decomposition of traffic data by vehicle type

The traffic flow data can also be decomposed by vehicle type, as in Figure 4, to better define preferred modes of travel of Dhaka commuters. Looking at CNG (Compressed Natural Gas) vehicles (three-wheeler commonly used as taxis), their traffic volume is at its minimum around 2 am; increases steeply around 5 am and 6 am reaching the morning peak by 9 am; decreases at 1 – 2 pm during lunch time; and increases again to reach its daily peak at 6 pm.

Figure 4. Traffic flows by vehicle type

Using data to infer commuters’ characteristics

Congestion data can also provide insights on commuters’ characteristics, including travel mode preferences. Figure 5 captures commuter preferences from the four main entry and exit arteries around Dhaka (New Airport Road, Buriganga Bridge, Gabtoli Bridge, Postogola Bridge).

Data was collected during the main commuting hours (8 to 9 in the morning and 5 to 6 in the evening). The figures only include vehicles used for public and private commuting.

As illustrated by the map, Airport Road is the most heavily trafficked road during peak commuting hours, followed by Gabtoli Bridge, Postogola, and Buriganga Bridge. Vehicle data from Figure 5 can be used to infer differences in socioeconomic characteristics of commuters. Airport road commuters are, on average, wealthier; the average commuter uses cars and private transport services. The preferences in modes of transport of Gabtoli commuters appear to be more disparate relative to Postogola and Buriganga Bridge commuters. Airport Road and Gabtoli commuters also have a greater supply of public transport services than Buriganga and Postogola Bridge, where supply is minimal. Gabtoli commuters are wealthier than Postogola and Buriganga Bridge commuters, as highlighted by the relatively higher proportion of private transport vehicles, including motorcycles and cars.

Figure 5. Commuter preferences from the four main entry and exit arteries around Dhaka city

More systematic data gathering could lead to better policy designs

Very little of the existing research on transportation choices in Dhaka use such comprehensive empirical data. Gathering this data more systematically can support better congestion policies. There are however, limitations some of which could be addressed with basic statistical tools, but others might require more effort with data-sharing and collection processes.

Better data would equip planners to understand the composition of traffic flows, and seasonal and other drivers of congestion, all of which should shape policy priorities for road investment and expenditures.

RFID stations currently only capture data from vehicles with digital plates (only about 35% of vehicles in Dhaka have digital plates). As might be expected, some types of vehicles are more likely to carry digital plates which will bias collected data. More information is needed on the proportion of vehicles out of the total population that have digital plates. Weighted data better captures the composition of vehicles in the total population.

Capturing data at a regular frequency can identify weekly, monthly and seasonal determinants of traffic congestion. Weekly data collected for each month will provide more granular variations in congestion.

Higher congestion may lead to under-counting – traffic jams increase immobility, reducing the number of times a vehicle pass through RFID sensors. Data on average hourly travelling speeds (measured as the time taken by a vehicle to travel between two proximate RFID stations) can better depict the actual severity of congestion. Comparing this data to non-congested times or against other factors such as large public events and weather conditions can potentially answer questions on how big of an impact other factors can have on congestion levels.


Cities are today the main engines of economic growth. However, failure to address the downsides of urbanisation may deter the ability of cities to achieve their full growth potential.

Congestion is a significant detractor of Dhaka’s productivity. There is a lot that the research community can do, but we need data to help us to be clear-sighted about the problems if we are to help effectively.

A version of this column has appeared on the IGC Blog.

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