Road traffic congestion in the developing world

Jain, Vipin; Sharma, Ashlesh; Subramanian, Lakshminarayanan

doi:10.1145/2160601.2160616

Cited by 140 publications

(71 citation statements)

References 4 publications

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“…al. [1] have experienced the histogram values of the roads in a day time conditions between 135 and 165. According to this work, the congestion values on the road is changed based on this values.…”

Section: Histogram Based Traffic Congestion Detectionmentioning

confidence: 99%

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Performance Measurements of Feature Tracking and Histogram Based Traffic Congestion Algorithms

Altun¹,

Aksoy²

2015

IJCSIT

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Section: Histogram Based Traffic Congestion Detectionmentioning

confidence: 99%

“…Vipin Jain et. al develop an algorithm to detect congestion level in the road traffic [1]. In this work, Jain et.…”

Section: Introductionmentioning

confidence: 99%

Performance Measurements of Feature Tracking and Histogram Based Traffic Congestion Algorithms

Altun¹,

Aksoy²

2015

IJCSIT

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“…To partially alleviate this problem, we present a local decongestion protocol that coordinates traffic signal behavior within a small area and can locally prevent congestion collapse, sustaining time variant traffic bursts. Using a simulation-based analysis on simple road network topologies, we have shown that our local decongestion protocol can enhance road capacity and prevent congestion collapse in localized settings (Jain, Sharma, and Subramanian 2012).…”

Section: Vision-based Road Traffic Congestion Monitoringmentioning

confidence: 99%

Computational Sustainability and Artificial Intelligence in the Developing World

Quinn

Frías-Martínez

Subramanian³

2014

AI Magazine

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Articles36 AI MAGAZINE I t might be thought that artificial intelligence techniques or other types of computational methods are irrelevant in countries with few technological resources. As just one example of the possibilities, however, take road traffic in cities. The chaotic and spectacular road congestion that is characteristic of developing-world cities is a microcosm of opportunities for applying AI methods. The problems are mainly caused by inadequate infrastructure (for example, road layouts that have not changed significantly despite decades of economic growth, unsealed or pothole-strewn roads), and a lack of resources to monitor or control traffic (for example, scarce and possibly corrupt traffic police, rolling blackouts affecting traffic lights). Computational solutions might come in the form of ways to cheaply gather realtime data, to advise individuals or emergency vehicles on optimal routes, to dynamically redeploy a limited number of

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“…Road traffic jams continue to be a major problem in most cities around the world, especially in developing regions resulting in increased fuel wastage and monetary losses [3]. The forecast of Global Traffic Volume (GTV) shows that road traffic jams would quadruple between 1990 and 2050 [4].…”

Section: Introductionmentioning

confidence: 99%

Traffic Regulation at Critical Intersections: A Case Study of Odole Intersection, Akure, Ondo State, Nigeria

Aderinola¹,

Owolabi²

2016

OJCE

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Traffic congestion on major roads consequent upon existing bottlenecks at intersections is a major problem in Akure Metropolis. To change this trend, this research was carried out in order to design effective traffic control mechanisms at the "troubled spots" in the metropolis. Odole intersection was identified as one of the critical intersections during a reconnaissance survey and as such, selected for study. Data on geometric features were collected using Oedometer and Google Earth software. Peak and off-peak traffic volume data were collected between 7:30 and 8:30 am and between 12.00 noon and 1.00 pm respectively every other day using Cine camera. Furthermore, discharge headway and delay data were collected using stop watch. The geometric and traffic data collected were analysed using Microsoft Excel. An appraisal of Odole Intersection indicated that the major contributors to traffic are Motorcycles (70.88%) and Passenger Cars (28.72%). Other modes of transportation account for about 0.4% of vehicles traversing the intersection. The critical traffic volume at the intersection was over 4000 veh/hr and the average delay was 22 seconds. An Average delay of 22 seconds at the intersection was an indication that the operating level of service was C (i.e. fairly stable traffic condition with average delays attributable to traffic control by personnel). By juxtaposing the results of geometric and traffic data analyses with the pros and cons of various traffic control mechanisms, traffic control by signalisation was selected and designed to suit Odole intersection. In tandem with the results of this research, appropriate measures have been recommended to ameliorate traffic and commuting problems in the metropolis.

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Road traffic congestion in the developing world

Cited by 140 publications

References 4 publications

Performance Measurements of Feature Tracking and Histogram Based Traffic Congestion Algorithms

Performance Measurements of Feature Tracking and Histogram Based Traffic Congestion Algorithms

Computational Sustainability and Artificial Intelligence in the Developing World

Traffic Regulation at Critical Intersections: A Case Study of Odole Intersection, Akure, Ondo State, Nigeria

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