Road Traffic Congestion in the Developing World
|
Vipin Jain Polytechnic Institute of NYU vjain02@students.poly.edu |
Ashlesh Sharma New York University ashlesh@cs.nyu.edu |
Lakshminarayanan Subramanian New York University |
Road traffic jams continue to remain a major problem in most cities around the world, especially in developing regions resulting in massive delays, increased fuel wastage and monetary losses. Due to the poorly planned road networks, a common outcome in many developing regions is the presence of small critical areas which are common hot-spots for congestion; poor traffic management around these hotspots potentially results in elongated traffic jams. In this paper, we first present a simple automated image processing mechanism for detecting the congestion levels in road traffic by processing CCTV camera image feeds. Our algorithm is specifically designed for noisy traffic feeds with poor image quality. Based on live CCTV camera feeds from multiple traffic signals in Kenya and Brazil, we show evidence of this congestion collapse behavior lasting long time-periods across multiple locations. To partially alleviate this problem, we present a local de-congestion protocol that coordinates traffic signal behavior within a small area and can locally prevent congestion collapse sustaining time variant traffic bursts. Based on a simulation based analysis on simple network topologies, we show that our local de-congestion protocol can enhance road capacity and prevent congestion collapse in localized settings.
I.4.9 [Computing Methodologies]: Image Processing and Computer Vision—Applications; I.6.3 [Computing Methodologies]: Simulation and Modeling—Applications
General Terms
Algorithms, Measurement
Keywords
traffic congestion, traffic detection, congestion collapse, simulation
|
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. To copy otherwise, to republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. DEV ’12, March 11-12, Atlanta, GA Copyright |
Poor road traffic
management is the primary reason for extended periods of traffic congestion
throughout the world. As per Texas
Transportation Institute’s 2011 Mobility report [1], congestion in the US has increased substantially over the last 25 years with massive amounts of losses pertaining to time, fuel and money. Sã o Paulo, Brazil is known to experience the world’s worst traffic jams [32], where people are stuck for two to three hours everyday in traffic jams. The issue of traffic congestion has affected both the developing and developed economies to different degrees irrespective of the measures taken to curb the issue.
A common feature across road networks in many urban regions in the developing world is the presence of critical congestion areas; we refer to a critical congestion area as one where a network of roads converge and a large amount of traffic needs to traverse the common congestion area. As per free-flow traffic theory [43], a free flow traffic road segment can be associated with a traffic curve where the traffic exit rate is a function of the traffic density in the road segment. A free-flow road segment is known to exhibit a critical density point where any traffic input that pushes the density beyond the critical value can trigger a “spiralling effect” that results in the road segment operating at a low-capacity equilibrium point. Worse still, small traffic bursts over short time periods can potentially trigger the spiralling effect resulting in a congestion collapse. Many critical congestion areas in developing regions have poor traffic management systems that if any of these critical congestion areas hits a congestion collapse, the road network can result in a massive traffic jam for elongated time periods.
In this paper, our goal is to design mechanisms to detect the state of traffic congestion in and around critical congestion areas and also design simple preventive mechanisms to prevent critical congestion areas from hitting congestion collapse. In this paper, we describe a simple image processing algorithm that can be used to analyze CCTV video feeds from traffic cameras to detect congestion levels in real time. Using this algorithm, we show evidence of actual congestion collapse across multiple locations in São Paulo, Brazil and Nairobi, Kenya. Specifically, we show congestion collapse scenarios that last for multiple hours at important junctions in Nairobi and São Paulo. Our congestion detection image processing algorithms have been specifically designed for highly noisy traffic camera feeds and differ in spirit from conventional traffic image processing techniques which typically rely on high quality traffic images [41, 44, 39].
To partially alleviate this problem, we propose a local de-congestion protocol that coordinates traffic signal behavior within a possible critical congestion area to prevent the critical tipping point behavior. The goal of the local traffic signal coordination is to maintain the traffic density in the congestion area below the critical density value. Our local de-congestion protocol coordinates the traffic signals
Уважаемый посетитель!
Чтобы распечатать файл, скачайте его (в формате Word).
Ссылка на скачивание - внизу страницы.
