Agglomeration effects and productivity gains are among the most desired of urbanisation outcomes. The less desirable outcomes, lagging infrastructure, elevated crime rates, and greater congestion require more evidence to understand what does and does not work to be effectively addressed. This blog examines the challenges of congestion and traffic in Dhaka, and it explores the potential for data to uncover evidenced-based policy designs that can effectively mitigate the downsides of congestion

Sebastio, Filippo

LSE Research Online

20/06/2017 Data-Jam: Could data reduce road congestion in Dhaka? - IGChttp://www.theigc.org/blog/data-jam-could-congestion-data-reduce-road-congestion-in-dhaka/ 1/6Authors Filippo SebastioResearcherPublished on: 26 Jan 2016Countries: BangladeshResearch themes: Cities Data-Jam: Could data reduceroad congestion in Dhaka?Agglomeration effects and productivity gainsare among the most desired of urbanisationoutcomes. The less desirable outcomes,lagging infrastructure, elevated crime rates,and greater congestion requiremore evidence to understand what does anddoes not work to be effectivelyaddressed. This blog examines thechallenges of congestion and trafﬁc in Dhaka,and it explores the potential for datato uncover evidenced-based policy designsthat can effectively mitigate the downsides ofcongestion.Congestion and urban mobilityDeveloping countries are urbanising at impressive rates withtrends set to continue. Urbanisation is an instrumental steppingstone for countries moving from poverty to prosperity. In fact,the relationship between urbanisation and income is strongertoday than it was in 1960. As Figure 1 illustrates, on average,higher levels of urbanisation in 1960 are associated with fastergrowth rates. This might reflect the impact that higher densityhas on productivity and employment.Figure 1: Urbanisation and the log change in real GDP per capitaDespite the potential to drive growth, one cannot ignore theconsiderable downsides that come with increased density,20/06/2017 Data-Jam: Could data reduce road congestion in Dhaka? - IGChttp://www.theigc.org/blog/data-jam-could-congestion-data-reduce-road-congestion-in-dhaka/ 2/6“considerable downsides that come with increased density,including congestion, public health and environmentalchallenges, and insufficient affordable housing stocks. In therecent decades, Dhaka has faced challenged all of thesechallenges.Tackling these downsides require effective urban governancewith a better understanding of the scope and magnitude of thegaps and limitations of existing infrastructure and public servicedelivery.Economic density of Bangladesh’s urban areas (GDP or value-added per square km) is low in comparison to global averages.One of the reasons for the economic under-performance is theurban congestion which constrains local productivity.According to the Bangladesh Road and Transport Authority(BRTA), every year around 37,000 cars are added to Dhaka’sroads, of which 80% are private cars. The number of privatecars is likely to grow further given that currently only 10% ofDhaka’s commuters own one. The road network in Dhaka isnearly 3,000 km with 200 km primary, 110 km secondary, 50km feeder, 2640 km narrow roads and few alternativeconnector roads. The proportion of road surface to built-uparea is approximately 7%, much lower than the 25%recommended for a good city planning. Policies are limited andenforcement is even weaker. There are more than 100 openstreet markets 3,000 shopping malls all built alongside roadswithout adequate parking provisions.Richer data produces more in depth analysis that canbetter pinpoint of congestionCongestion is not an exclusively developing country problem –London and New York have traffic jams just as severe as thosein Delhi and Lagos. Without proper planning, congestionworsens as incomes rise, as increasing proportions of thepopulation begin affording vehicles.At minimum, an effective public sector should invest in andmaintain a basic systems of public transportation infrastructureand implicitly (or explicitly) establish usage rules and pricing.This requires balancing accessibility and sustainability oftransportation.Better data would equip planners to understand thecomposition of traffic flows, seasonal and other driversof congestion, all of which should shape policy prioritiesfor road investment and expenditures. ”The richness of the data that can be collected allows for analysisand sheds light on the causes of congestion in Dhaka,potentially could spearhead an open evidence-based discussionon the potential solutions. Daily data can be merged with socio-economic and event data to pattern travel mode choices andevidence the potential causes of traffic jams. This evidence canbe used to tailor policy interventions and assess implementationfeasibility.20/06/2017 Data-Jam: Could data reduce road congestion in Dhaka? - IGChttp://www.theigc.org/blog/data-jam-could-congestion-data-reduce-road-congestion-in-dhaka/ 3/6Data and policy – a promising partnershipThe following tables and graphs, created for a collaborativeproject between the IGC and BRTA and serve as an example ofthe kinds of congestion data that can be collected and analysedas evidence of the gaps within existing transport infrastructure.The map in Figure 2 identifies the location of the RFDI towers –traffic data collector sensors that count the number of vehicles –and illustrates traffic volumes observed 1 January, 2014. Themost trafficked areas are New Airport Road, Mohakhali,Farmgate, the main arteries of the city.Figure 2: Trafﬁc Densities around Dhaka, 1 January 2014Note: The diameter of the circles correspond to traffic volumesHourly traffic volume data in Figure 3 (below) provides a moregranular analysis of traffic flows on the same day. The volumepeaks during the two main commuting times, 9 am and 6 pm.Figure 3: Hourly trafﬁc volumes in Dhaka, 1 January 2014Trafﬁc congestion, contributions by vehicle typeThe traffic flow data can also be decomposed by vehicle type, asin Figure 4, to better define preferred modes of travel of Dhakacommuters. Taking into consideration CNGs (three-wheelercommonly used as taxis), their traffic volume is at its minimumaround 2 am, and increases steeply around 5 am and 6 amreaching the morning peak by 9 am, decreases at 1 – 2 pmduring lunch time, and increases again to reach its daily peak at20/06/2017 Data-Jam: Could data reduce road congestion in Dhaka? - IGChttp://www.theigc.org/blog/data-jam-could-congestion-data-reduce-road-congestion-in-dhaka/ 4/6during lunch time, and increases again to reach its daily peak at6 pm.Figure 4: Trafﬁc ﬂows by vehicle typeData can be used to infer commuters’ characteristicsCongestion data can also provide insights on commuters’characteristics, including transport and travel mode preferences.Figure 5 captures commuter preferences from the four mainentry and exit arteries around Dhaka (New Airport Road,Buriganga Bridge, Gabtoli Bridge, Postogola Bridge).Data was collected during the main commuting hours (8 and 9in the morning and 5 and 6 in the evening). The graphs onlyinclude vehicles used for public and private commuting.As illustrated by the map, Airport Road is the most heavilytrafficked road during peak commuting hours, followed byGabtoli Bridge, Postogola, and Buriganga Bridge. Vehicle datafrom the table can be used to infer differences in socio-economic characteristics of commuters. Airport roadcommuters are on average wealthier, average commutersuse cars and private transports services. The preferences inmodes of transport of Gabtoli commuters appear to be moredisparate relative to Postogola and Buriganga Bridgecommuters. Airport Road and Gabtoli commuters also have agreater supply of public transport services than Buriganga andPostogola Bridge, where supply is minimal. Gabtoli commutersare wealthier than Postogola and Buriganga Bridge commuters,as highlighted by the relatively higher proportion of privatetransport vehicles, including motorcycles and cars.20/06/2017 Data-Jam: Could data reduce road congestion in Dhaka? - IGChttp://www.theigc.org/blog/data-jam-could-congestion-data-reduce-road-congestion-in-dhaka/ 5/6“More systematic data gathering could lead to better policydesignsVery little of the existing research on transportation choices inDhaka use such comprehensive empirical data. Gathering thisdata more systematically can support better congestion policiesif it can be more systematically gathered. There are however,limitations some of which could be addressed with basicstatistical tools, but others might require more effort with datasharing and collection processes.One of the reasons for the economic under-performance is the urban congestion which constrainslocal productivity. ”Better data would equip planners to understand thecomposition of traffic flows, seasonal and other drivers ofcongestion, all of which should shape policy priorities for roadinvestment and expenditures. Improving coverage andcollection of congestion data will be particularly important inimproving predictive models.RFID stations currently only capture data from vehicles withdigital plates (only ~35% of vehicles in Dhaka have digitalplates). As might be expected, some types of vehicles are morelikely to carry digital plates which will bias collected data. Moreinformation is needed on the proportion of vehicles out of thetotal population that have digital plates. Weighted data bettercaptures 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 moregranular variations in congestion.Intensity of congestion affects the efﬁcacy ofmeasurementsHigher congestion may lead to under-counting – traffic jamsincrease immobility, reducing the number of times a vehiclepass through RFID sensors. Data on average hourly travellingspeeds (measured as the time a vehicles travels between twoproximate RFID stations can better depict the actual severity ofcongestion. Comparing this data to non-congested times oragainst other factors such as large public events and weatherconditions can potentially answer to questions on how big of animpact other factors can have on congestion levels.ConclusionCities are today the main engines of economic growth.20/06/2017 Data-Jam: Could data reduce road congestion in Dhaka? - IGChttp://www.theigc.org/blog/data-jam-could-congestion-data-reduce-road-congestion-in-dhaka/ 6/6“Cities are today the main engines of economic growth.However, failure to address the downsides of urbanisation maydeter the ability of cities to achieve their full growth potential.Improving coverage and collection of congestiondata will be particularly important in improvingpredictive models. ”Congestion is a significant detractor of Dhaka’s productivity.There is a lot that the research community can do, but we needdata to help us to be clear- sighted about the problems if we areto help effectively.

Data-Jam: could data reduce road congestion in Dhaka?

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