Online Implementation and Evaluation of Weather-Responsive Coordinated Signal Timing Operations

Chen, Ying; Mahmassani, Hani S.; Hong, Zihan; Hou, Tian; Kim, Jiwon; Halat, Hooram; Alfelor, Roemer

doi:10.3141/2488-08

Cited by 6 publications

(4 citation statements)

References 16 publications

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“…Efforts have been made to integrate the weather effects into decision support tools (13,18,23). The developed capable and reliable tools focus primarily on (a) real-time TrEPS, incorporating field observations and estimating and predicting network states to enable implementation and evaluation of online traffic management; (b) implementation of multimodal dynamic network simulation of travelers and vehicles given predicted traveler information; and (c) weather-related advisory and control measures for integrated real-time WRTM with a TrEPS model (13,14,16,17,19,24).…”

Section: Weather-responsive Strategiesmentioning

confidence: 99%

“…As follow-up research for WRTM and ATDM, this paper presents a framework for a simulation-based test bed that emulates day-today traffic scenarios and evaluates the effectiveness of the simulated operations. The test bed was developed with traffic estimation and prediction system (TrEPS) capabilities that involve predictive traffic information in system evaluation and decision-making procedures to support traffic management for multiple scenarios (18,19). The primary objectives of this paper are to develop predictive ATDM and WRTM strategies and to evaluate the effectiveness by integrating offline real-world simulation with online prediction of the traffic network.…”

mentioning

confidence: 99%

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Effectiveness of Predictive Weather-Related Active Transportation and Demand Management Strategies for Network Management

Hong

Mahmassani

et al. 2017

Transportation Research Record: Journal of the Transportation R

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This paper presents the development, implementation, and evaluation of predictive active transportation and demand management (ATDM) and weather-responsive traffic management (WRTM) strategies to support operations for weather-affected traffic conditions with traffic estimation and prediction system models. First, the problem is defined as a dynamic process of traffic system evolution under the impact of operational conditions and management strategies (interventions). A list of research questions to be addressed is provided. Second, a systematic framework for implementing and evaluating predictive weather-related ATDM strategies is illustrated. The framework consists of an offline model that simulates and evaluates the traffic operations and an online model that predicts traffic conditions and transits information to the offline model to generate or adjust traffic management strategies. Next, the detailed description and the logic design of ATDM and WRTM strategies to be evaluated are proposed. To determine effectiveness, the selection of strategy combination and sensitivity of operational features are assessed with a series of experiments implemented with a locally calibrated network in the Chicago, Illinois, area. The analysis results confirm the models’ ability to replicate observed traffic patterns and to evaluate the system performance across operational conditions. The results confirm the effectiveness of the predictive strategies tested in managing and improving traffic performance under adverse weather conditions. The results also verify that, with the appropriate operational settings and synergistic combination of strategies, weather-related ATDM strategies can generate maximal effectiveness to improve traffic performance.

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Section: Weather-responsive Strategiesmentioning

confidence: 99%

mentioning

confidence: 99%

Effectiveness of Predictive Weather-Related Active Transportation and Demand Management Strategies for Network Management

Hong

Mahmassani

et al. 2017

Transportation Research Record: Journal of the Transportation R

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“…Traffic responsive plan selection (TRPS) and adaptive traffic control systems designed and deployed over the past several decades were intended to provide quicker response to constantly varying traffic conditions (2). A recent application included development of a real-time weather-responsive signal control (3). These advances attempted to incorporate more robustness into designed signal timing plans.…”

mentioning

confidence: 99%

Assessment of the Robustness of Signal Timing Plans in an Arterial Corridor Through Seasonal Variation of Traffic Flows

Ostojic

Stevanovic

Jolovic

et al. 2017

Transportation Research Record: Journal of the Transportation R

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Arterial traffic signal systems, predominantly in the United States, deploy multiple signal timing plans to account for daily variability of traffic demand. Those types of traffic flow deviations should be anticipated when timing plans are designed and, therefore, serviced satisfactorily. When traffic flow patterns are no longer predictable, a predetermined time-of-day (TOD) plan may no longer be the optimal one. This research aimed to examine signal timing optimality by applying a method similar to the selection of a traffic responsive plan to recognize automatically the best timing plan suited to current traffic conditions. The proposed method attempted to determine whether the optimality of signal timing settings could have been effectively estimated when systematic detector counts of the major approach were available. The study used 4 months of data from field microwave detectors coupled with data of turning-movement counts obtained over several days. The findings show that TOD signal timing plans mainly depended on adequate data collection that best describes a specific set of traffic conditions. Thus, the designed plan was as optimal as the related traffic information was reliable, whereas a problem arose in the case of limited-availability and low-quality data. New technologies are capable of collecting and storing massive amounts of data. Even if the granularity of collected data is low, the data can be used to improve traffic performance (i.e., reduce corridor delay). This realization could be of particular importance to traffic agencies that have installed, or plan to install, new field devices.

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“…It provides a controlled environment to conduct experiments and identify issues prior to implementation of the system. Applications of the traffic decision support platforms include: optimal signal timing (Chen et al, 2015), integrated corridor management (Hashemi and Abdelghany, 2016), and incident management (Zografos et al, 2002).…”

Section: Decision Support Platformmentioning

confidence: 99%

Decision support platform for urban rail systems : real-time crowding prediction and information generation

Noursalehi¹

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Demand for the public transit systems has witnessed a steady growth over the last decade in many densely populated cities around the world. However, capacity has not always matched this increased demand. As such, passengers experience long waiting times and are denied boarding during the peak hours. Crowded platforms, and the subsequent customer dissatisfaction and safety issues, have become a serious concern. Another development is the rapid pace of technological innovations. Smartphones have enabled companies to interact with their customers directly and seamlessly, providing personalized information in real-time. The prevalence of such applications has raised the expectation among passengers to receive a similar type of information about their expected trip conditions. This presents a unique opportunity for transit system operators. Real-time information can potentially influence passengers' trip making decisions. Some users might decide to shift their arrival time as to avoid overcrowded stations. Similarly, some passengers might decide to wait for a train after the one that is currently arriving at a station, if they are informed that this action will result in a better trip experience (such as less crowding on the train). This dissertation proposes and implements a predictive decision support platform that addresses both operations control and customer information needs. The capabilities of this platform can be used to foresee expected overcrowded platforms and trains. Real-time predictive control measures can be implemented to prevent such conditions from arising. Second, it generates information on the expected probability of being able to board upcoming trains, which can be communicated to passengers both inside the system and those about to travel. Using this information, passengers can make better informed decisions as to which train to board for maximizing their utility. The decision support comprises two major components: a demand prediction module, and an on-line simulation. The first module provides short-term prediction of the number of passengers arriving at each station, and their final destinations.

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Online Implementation and Evaluation of Weather-Responsive Coordinated Signal Timing Operations

Cited by 6 publications

References 16 publications

Effectiveness of Predictive Weather-Related Active Transportation and Demand Management Strategies for Network Management

Effectiveness of Predictive Weather-Related Active Transportation and Demand Management Strategies for Network Management

Assessment of the Robustness of Signal Timing Plans in an Arterial Corridor Through Seasonal Variation of Traffic Flows

Decision support platform for urban rail systems : real-time crowding prediction and information generation

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