Utilizing Low-Ping Frequency Vehicle Trajectory Data to Characterize Delay at Traffic Signals

Waddell, Jonathan M.; Remias, Stephen M.; Kirsch, Jenna N.; Trepanier, Ted

doi:10.1061/jtepbs.0000382

Cited by 13 publications

(6 citation statements)

References 16 publications

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“…In locations where location-based sensor data is not available, researchers have used probe vehicle data-based delay estimation techniques. Researchers have used GPS data [25,26], cellular device data [27][28][29], Bluetooth data [30][31][32], and Wi-Fi sensors data [33][34][35][36][37] as probe data for estimating delay under conditions where location-based data is not available. ough the probe-based approaches yield good estimates of delay even on roads with no location-based sensors, they require additional sensors like GPS sensors, Bluetooth sensors, or Wi-Fi sensors.…”

Section: Literature Reviewmentioning

confidence: 99%

Development of a Theoretical Delay Model for Heterogeneous and Less Lane-Disciplined Traffic Conditions

Khadhir

Bhaskar

Vanajakshi

et al. 2022

Journal of Advanced Transportation

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In developing countries with limited or no availability of traffic sensors, theoretical delay models are the most commonly used tool to estimate control delay at intersections. The traffic conditions in such countries are characterised by a large mix of vehicle types and limited or no lane discipline (Heterogeneous, Less Lane-Disciplined (HLLD) traffic conditions), resulting in significantly different traffic dynamics. This research develops a queueing theory-based theoretical delay model that explicitly incorporates HLLD traffic conditions’ characteristic features like lack of lane discipline, violation of the First-In-First-Out rule, and a large mix of vehicle types. A new saturation flow-based Passenger Car Equivalent (PCE) estimation methodology to address heterogeneity and a virtual lane estimation approach to address lack of lane-discipline are proposed. The developed model shows 64% lesser error in average control delay estimation compared to the in-practice delay estimation models under HLLD traffic conditions. The developed model is used for signal optimisation under HLLD traffic conditions and reductions of up to 24% in control delay in comparison to the in-practice signal timing approach are observed. The study also highlights the significance of knowing the variation of delay in addition to average delay and presents a simple approach to capture the variation in delay.

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Section: Literature Reviewmentioning

confidence: 99%

Development of a Theoretical Delay Model for Heterogeneous and Less Lane-Disciplined Traffic Conditions

Khadhir

Bhaskar

Vanajakshi

et al. 2022

Journal of Advanced Transportation

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“…CV date have also been used to develop GPS-based automated traffic signal performance measures (ATSPM) that have expanded coverage and scalability. A quick diagnosis of the immediate signal performance issues can be identified with as low as 0.04% [4]. The queue propagation around freeway bottleneck and congestion on arterial based on the percentage of slowmoving vehicles can be identified using CV data [5].…”

Section: Literature Review a Connected Vehicle Datamentioning

confidence: 99%

Framework for Highway Traffic Profiling using Connected Vehicle Data

Zhong¹,

Zhao²,

Dimitrijevic³

et al. 2022

Preprint

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The connected vehicle (CV) data could potentially revolutionize the traffic monitoring landscape as a new source of CV data that are collected exclusively from original equipment manufactures (OEMs) have emerged in the commercial market in recent years. Compared to existing CV data that are used by agencies, the new-generation of CV data have certain advantages including nearly ubiquitous coverage, high temporal resolution, high spatial accuracy, and enriched vehicle telematics data (e.g., hard braking events). This paper proposed a traffic profiling framework that target vehicle-level performance indexes across mobility, safety, riding comfort, traffic flow stability, and fuel consumption. The proof-of-concept study of a major interstate highway (i.e., I-280 NJ), using the CV data, illustrates the feasibility of going beyond traditional aggregated traffic metrics. Lastly, potential applications for either historical analysis and even near real-time monitoring are discussed. The proposed framework can be easily scaled and is particularly valuable for agencies that wish to systemically monitoring regional or statewide roadways without substantial investment on infrastructure-based sensing (and the associated on-going maintenance costs).

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“…Recent advancements in third-party commercially-available connected vehicle trajectory data have enabled practitioners to measure travel time, reliability, and delay performance for general traffic vehicles at high spatial and temporal fidelity [36] [37]. It is now possible to quantitatively compare mode performance with bus AVL data to assess intersection approaches with high delay, schedule adherence/on-time performance, travel time and reliability along a route by time-of-day.…”

Section: IIImentioning

confidence: 99%

“…There are several well-documented methodologies in the literature that discuss the estimation of total delay from vehicle trajectories [36] [37] [40] [41]. Control delay for buses can also be computed in a similar way, however there are additional factors that need to be considered:…”

Section: Average Bus Delay At Intersectionsmentioning

confidence: 99%

Integrated Performance Measures for Bus Rapid Transit System and Traffic Signal Systems Using Trajectory Data

Mathew¹,

Li²,

Saldivar-Carrranza³

et al. 2022

JTTs

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Bus rapid transit (BRT) systems have been implemented in many cities over the past two decades. Widespread adoption of General Transit Feed Specification (GTFS), the deployment of high-fidelity bus GPS data tracking, and anonymized high-fidelity connected vehicle data from private vehicles have provided new opportunities for performance measures that can be used by both transit agencies and traffic signal system operators. This paper describes the use of trajectory-based data to develop performance measures for a BRT system in Indianapolis, Indiana. Over 3 million data records during the 3-month period between March and May 2022 are analyzed to develop visualizations and performance metrics. A methodology to estimate the average delay and schedule adherence is presented along a route comprised of 74 signals and 28 bus stations. Additionally, this research demonstrates how these performance measures can be used to evaluate dedicated and non-dedicated bus lanes with general traffic. Travel times and reliability of buses are compared with nearly 30 million private vehicle trips. Results show that median travel time for buses on dedicated bi-directional lanes is within one minute of general traffic and during peak periods the buses are often faster. Schedule adherence was observed to be more challenging, with approximately 3% of buses arriving within 1 minute on average during the 5AM hour and 5% of buses arriving 6 -9 minutes late during the 5PM hour. The framework and performance measures presented in this research provide agencies and transportation professionals with tools to identify opportunities for adjustments and to justify investment decisions.

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Utilizing Low-Ping Frequency Vehicle Trajectory Data to Characterize Delay at Traffic Signals

Cited by 13 publications

References 16 publications

Development of a Theoretical Delay Model for Heterogeneous and Less Lane-Disciplined Traffic Conditions

Development of a Theoretical Delay Model for Heterogeneous and Less Lane-Disciplined Traffic Conditions

Framework for Highway Traffic Profiling using Connected Vehicle Data

Integrated Performance Measures for Bus Rapid Transit System and Traffic Signal Systems Using Trajectory Data

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