Discovering Traffic Bottlenecks in an Urban Network by Spatiotemporal Data Mining on Location-Based Services

Lee, Wei-Hsun; Tseng, Shian‐Shyong; Shieh, Jin-Lih; Chen, Hsiao-Han

doi:10.1109/tits.2011.2144586

Cited by 67 publications

(31 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Recent research efforts have shown the ability to model congestion prediction and control of road transportation [1][2][3][4][5] and the metro [6][7][8][9][10]. Sun et al [11] explored the hazardous materials route problem in the road-rail multimodal transportation network with a hub-and-spoke structure.…”

Section: Literature Reviewmentioning

confidence: 99%

A Time-Space Network Model Based on a Train Diagram for Predicting and Controlling the Traffic Congestion in a Station Caused by an Emergency

2019

Symmetry

View full text Add to dashboard Cite

Timely predicting and controlling the traffic congestion in a station caused by an emergency is an important task in railway emergency management. However, traffic forecasting in an emergency is subject to a dynamic service network, with uncertainty surrounding elements such as the capacity of the transport network, schedules, and plans. Accurate traffic forecasting is difficult. This paper proposes a practical time-space network model based on a train diagram for predicting and controlling the traffic congestion in a station caused by an emergency. Based on the train diagram, we constructed a symmetric time-space network for the first time by considering the transition of the railcar state. On this basis, an improved A* algorithm based on the railcar flow route was proposed to generate feasible path sets and a dynamic railcar flow distribution model was built to simulate the railcar flow distribution process in an emergency. In our numerical studies, these output results of our proposed model can be used to control traffic congestion.

show abstract

Section: Literature Reviewmentioning

confidence: 99%

A Time-Space Network Model Based on a Train Diagram for Predicting and Controlling the Traffic Congestion in a Station Caused by an Emergency

2019

Symmetry

View full text Add to dashboard Cite

show abstract

“…The paper discovers traffic bottle necks in spatiotemporal coordinates (Spatial-Location; Temporal-Time) [5]. The sensing is done through location based services.…”

Section: R S Parmar Et Al Journal Of Transportation Technologiesmentioning

confidence: 99%

A Model with Traffic Routers, Dynamically Managing Signal Phases to Address Traffic Congestion in Real Time

Parmar¹,

Trivedi²,

Stevanović³

2018

JTTs

View full text Add to dashboard Cite

On-road Vehicular traffic congestion has detrimental effect on three lifelines: Economy, Productivity and Pollution (EPP). With ever increasing population of vehicles on road, traffic congestion is a major challenge to the economy, productivity and pollution, notwithstanding continuous developments in alternative fuels, alternative sources of energy. The research develops accurate and precise model in real time which computes congestion detection, dynamic signaling algorithm to evenly distribute vehicle densities while ensuring avoidance of starvation and deadlock situation. The model incorporates road segment length and breadth, quality and achievable average speed to compute road capacity. Vehicles installed with GPS enabled devices provide their location, which enables computing road occupancy. Road occupancy is evaluated based on number of vehicles as well as area occupied by vehicles. Ratio of road occupancy and road capacity provides congestion index important to compute signal phases. The algorithm ensures every direction is serviced once during a signaling cycle ensuring no starvation. Secondly, the definition of minimum and maximum signal timings ensures against dead lock situation. A simulator is developed to validate the proposition and proves it can ease congestion by more than 50% which is better than any of the contemporary approaches offering 15% improvement. In case of higher congestion index, alternate routes are suggested based on evaluation of traffic density graphs for shortest route or knowledge database. The algorithm to compute shortest route is optimized drastically, reducing computation cost to 3 2N * vis-à-vis computation cost of N 2 by classical algorithms. The proposal brings down the cost of implementation per traffic junction from USD 30,000 to USD 2000.

show abstract

“…Some areas of data mining applications are: civil engineering (Guo, Hu, & Peng, 2011;Chunchun, Nianxue, Xiaohong, & Wenzhong, 2011;Lee, Tseng, Shieh, & Chen 2011); medicine (Koh & Tan, 2011;Shouman, Turner, & Stocker, 2012); education (Asif, Merceron, & Pathan, 2012;Hung, Hsu, & Rice, 2012;Yadav, Bharadwaj, & Pal, 2012); banking and finance (Kwak, Shi, & Kou, 2012;Prasad & Madhavi, 2012;Ravi, Nekuri, & Rao, 2012).…”

Section: Introductionmentioning

confidence: 99%

Logistic groups in the Republic of Panama

et al. 2015

View full text Add to dashboard Cite

A map of the logistic groups in the Republic of Panama and complementary maps of the distribution of the population density, GDP density, the economic activity 'transport, storage and communications', and the primary and secondary road network are presented. The clusters of territorial groups on each map were analyzed and created using the 'decision tree' data mining method in order to determine territorial groups with similar characteristics, including logistics infrastructure and nodes, socioeconomics and the environmental situation. At the reference scale of 1:800,000 these maps represent a planning tool to decision makers in land use matters. ARTICLE HISTORY

show abstract

Discovering Traffic Bottlenecks in an Urban Network by Spatiotemporal Data Mining on Location-Based Services

Cited by 67 publications

References 15 publications

A Time-Space Network Model Based on a Train Diagram for Predicting and Controlling the Traffic Congestion in a Station Caused by an Emergency

A Time-Space Network Model Based on a Train Diagram for Predicting and Controlling the Traffic Congestion in a Station Caused by an Emergency

A Model with Traffic Routers, Dynamically Managing Signal Phases to Address Traffic Congestion in Real Time

Logistic groups in the Republic of Panama

Contact Info

Product

Resources

About