Chula-SSS: Developmental Framework for Signal Actuated Logics on SUMO Platform in Over-saturated Sathorn Road Network Scenario

Aswakul, Chaodit; Watarakitpaisarn, Sorawee; Komolkiti, Patrachart; Krisanachantara, Chonti; Techakittiroj, Kittiphan

doi:10.29007/t895

Cited by 7 publications

(11 citation statements)

References 5 publications

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“…In particular, we use Chula-Sathorn SUMO Simulator (Chula-SSS) which is an educational tool for traffic police and traffic engineers. This tool [8] has been recently introduced from the Chulalongkorn University's Sathorn Model project in Bangkok (Thailand) for traffic police and traffic engineer deployment. Chula-SSS supports two calibrated datasets (morning and evening) for the Sathorn road network area as shown in Fig.…”

Section: Sumo Simulation Environmentmentioning

confidence: 99%

“…In that paper, the authors has considered only small urban road network and they mainly highlighted on multi-agent control scenario to detect the incident. Unlike [7], the main purpose of our study is rather to analyse traffic patterns for practically calibrated complex urban road network, Chula-SSS [8]. Like [7], we use SVM to recognize and classify the anomalous events in nearby incident area such as upstream and downstream of the incident location as the incident might affect the nearby area.…”

Section: Introductionmentioning

confidence: 99%

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Traffic Anomaly Classification by Support Vector Machine with Radial Basis Function on Chula-SSS Urban Road Network

2019

Proceedings of 2019 the 9th International Workshop on Computer Science and Engineering

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Recognition of urban road traffic pattern is an important part of intelligent transportation systems .An enormous number of traffic data could be obtained with the develop ment of in formation techniques . This motivates the application of machine learn ing in the road traffic area, especially in traffic incident detection. Incident detection algorith m in the machine learning can be defined as a binary classification problem, where each occurrence is the traffic state on a road segment at a particular time. This paper is concerned with how to detect traffic anomaly patterns in an urban road network by using potential sensor data. In this paper, by using Simulation of Urban Mobility (SUM O) software, we have chosen to work on the Chula -Sathorn SUM O Simulator (Chula -SSS) dataset. SUMO enables users to simulate traffic networks and supports the traffic data by setting up conveniently simu lated lane area detectors. By using calibrated Chula -SSS dataset, anomaly traffic patterns have been generated and classified with the support vector mach ine algorithm with the radial basis function. The algorith m has been shown here to detect accurately of at least 87% (and 71 %) of the simulated lane-closure incidences, by rely ing on sensors fro m (i) within the incident area, and (ii) at the upstream as well as downstream areas adjacent to that incident link, respectively.

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Section: Sumo Simulation Environmentmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Traffic Anomaly Classification by Support Vector Machine with Radial Basis Function on Chula-SSS Urban Road Network

2019

Proceedings of 2019 the 9th International Workshop on Computer Science and Engineering

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“…The main phases as defined by the traffic police experts are phase 1, 3, and 5. However, based on the extracted heuristic signal actuated logics from human experts [3] as shown in Figure 4, extra phases which are the subset of other phases like phase 4 and 6 are also utilized in special cases to mitigate gridlock.…”

Section: Sathorn Road Infrastructurementioning

confidence: 99%

“…Various sensors have been installed along the upstream lanes approaching the critical Surasak Intersection by the project [21]. In early attempts to mimic real world congestions for what-if scenario studies, the work of Chula-Sathorn SUMO Simulator (Chula-SSS) [3] has been initiated to provide calibrated datasets over the Sathorn Road network in a microscopic road traffic simulation package Simulation of Urban MObility (SUMO) [15]. The datasets has been extensively calibrated by the root mean squared error (RMSE) of the actual link travel time in the weekday morning and evening rush hour from 6 AM to 9 AM and 3 PM to 7 PM respectively.…”

Section: Introductionmentioning

confidence: 99%

“…The current traffic indicators gained through the expert's experience are the queue length and vehicle minimum gap. Although heuristic signal actuated logics were extracted from human experts as an attempt to standardize the process [3], human expertise is still heavily relied on and current tools are utilized for visualization, simulation, and further analysis. In overall, hence, the traffic signal light control problem remains an open challenge to explore further towards an optimization and automation within the present traffic signal control policies.…”

Section: Introductionmentioning

confidence: 99%

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Reinforcement Learning Agent under Partial Observability for Traffic Light Control in Presence of Gridlocks

Horsuwan¹,

Aswakul²

EPiC Series in Computing

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Bangkok is notorious for its chronic traffic congestion due to the rapid urbanization and the haphazard city plan. The Sathorn Road network area stands to be one of the most critical areas where gridlocks are a normal occurrence during rush hours. This stems from the high volume of demand imposed by the dense geographical placement of 3 big educational institutions and the insufficient link capacity with strict routes. Current solutions place heavy reliance on human traffic control expertises to prevent and disentangle gridlocks by consecutively releasing each queue length spillback through inter-junction coordination. A calibrated dataset of the Sathorn Road network area in a microscopic road traffic simulation package SUMO (Simulation of Urban MObility) is provided in the work of Chula-Sathorn SUMO Simulator (Chula-SSS). In this paper, we aim to utilize the Chula-SSS dataset with extended vehicle flows and gridlocks in order to further optimize the present traffic signal control policies with reinforcement learning approaches by an artificial agent. Reinforcement learning has been successful in a variety of domains over the past few years. While a number of researches exist on using reinforcement learning with adaptive traffic light control, existing studies often lack pragmatic considerations concerning application to the physical world especially for the traffic system infrastructure in developing countries, which suffer from constraints imposed from economic factors. The resultant limitation of the agent's partial observability of the whole network state at any specific time is imperative and cannot be overlooked. With such partial observability constraints, this paper has reported an investigation on applying the Ape-X Deep Q-Network agent at the critical junction in the morning rush hours from 6 AM to 9 AM with practically occasional presence of gridlocks. The obtainable results have shown a potential value of the agent's ability to learn despite physical limitations in the traffic light control at the considered intersection within the Sathorn gridlock area. This suggests a possibility of further investigations on agent applicability in trying to mitigate complex interconnected gridlocks in the future.

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Real-world sensor dataset for city inbound-outbound critical intersection analysis

et al. 2022

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This paper reports the release of dataset that describes the critical city inbound-outbound intersection in the central business district’s Sathorn area of Bangkok, Thailand. The available traffic volume and occupancy are captured by sensors located on the Sathorn area’s links. Induction loop coil sensors and thermal and CCTV cameras have been installed at the approaching links of the critical Sathorn-Surasak intersection. Traffic volume data have been collected from the CCTV cameras every 5 s from 2016 to 2019, and occupancy and volume data have been collected from the loop coil sensors every 5 s from May to September 2016 during a social experiment that was part of Sathorn Model project. Occupancy and volume data have also been collected from the thermal cameras every 5 s from May to June 2016. The dataset provides temporal and spatial coverage of Sathorn Road’s primary urban areas, including weekdays, weekends, and public holidays. This dataset can be resources for research on traffic state estimation, traffic light control optimization, and the analysis of critical intersections.

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Chula-SSS: Developmental Framework for Signal Actuated Logics on SUMO Platform in Over-saturated Sathorn Road Network Scenario

Cited by 7 publications

References 5 publications

Traffic Anomaly Classification by Support Vector Machine with Radial Basis Function on Chula-SSS Urban Road Network

Traffic Anomaly Classification by Support Vector Machine with Radial Basis Function on Chula-SSS Urban Road Network

Reinforcement Learning Agent under Partial Observability for Traffic Light Control in Presence of Gridlocks

Real-world sensor dataset for city inbound-outbound critical intersection analysis

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