Abstract
Given the fact that the existing literature lacks the real-time estimation of road capacity and incident location using data from inductance loop detectors, a data-driven framework is proposed in this study for real-time incident detection, as well as road capacity and incident location estimation. The proposed algorithm for incident detection is developed based on the variation in traffic flow parameters acquired from inductance loop detectors. Threshold values of speed and occupancy are determined for incident detection based on the PeMS database. The detection of the incident is followed by the real-time road capacity and incident location estimation using a Cell Transmission Model (CTM) based approach. The data of several incidents were downloaded from PeMS and used for the development of the proposed framework presented in this study. Results show that the developed framework detects the incident and estimates the reduced capacity accurately. The location of the incident is estimated with an overall accuracy of 92.5%. The performance of the proposed framework can be further improved by incorporating the effect of the on-ramps, off-ramps, and high-occupancy lanes, as well as by modeling each lane separately.
Highlights
An incident is among the most unfavorable events that significantly affect the roadway capacity and decrease the reliability of the system. e impact of disruption due to incident could be minimized by implementing real-time intervention strategies. e effectiveness of the intervention measures depends on accurate real-time information about the location, duration, and impact of the incident.e main problem caused by the incident is not the lack of capacity but the temporary reduction of capacity due to the incident [1]
Incident Detection Using PeMS Data. e open-source data of the Department of Transportation California (PeMS) was used for the application of the model in a real environment. e incident details mentioned in Table 1 were collected from the incident log of the PeMS database. e framework proposed in Figure 1 is applied to capture the selected incidents listed in Table 1. e results of incident detections are shown in Table 5, which compares the incident time reported by PeMS data with the time of incident detected by the algorithm
E average difference in the reported incident time and the incident time detected by the algorithm is 9 minutes. e least difference of 1 minute was observed for the incident that occurred at I105-E. e highest difference of about 27 minutes was observed for the incident reported at I80-E on January 15, 2020, at 18 : 58 hrs. e incident reported on SR24-E was detected 10 minutes before its reported time. is shows the discrepancy in the incident reporting time, which must have been recorded with some error. e change in traffic flow parameters at the selected sensor of SR24-E indicates the occurrence of the incident before the reported time, which could be due to the inaccurate field report of the incident
Summary
An incident is among the most unfavorable events that significantly affect the roadway capacity and decrease the reliability of the system. e impact of disruption due to incident could be minimized by implementing real-time intervention strategies. e effectiveness of the intervention measures depends on accurate real-time information about the location, duration, and impact of the incident. E estimation of the location of an incident enables the traffic control system to divert the flow to an alternate route to reduce congestion on the affected road. An explicit framework for real-time road capacity and incident location estimation using loop detector data does not exist in the available literature. Is study develops a framework for incident detection, capacity estimation, and estimation of incident location based on data of traffic flow parameters obtained from loop detectors. E traffic flow parameters measured at the loop detectors are affected by the incident due to the reduction of capacity. E variation of traffic flow parameters due to the incident can be used for incident detection, estimation of capacity, and estimating the location of the incident in a road section between two consecutive loop detectors. Variation in the traffic flow parameters recorded at the sensor could be due to the factors other than the incident such as the daily variation, weekly variation, or variation due to the weather impact. erefore, an appropriate incident
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