To explore traveler transfer decisions for different purposes, a transfer decision model allowing psychological comprehensive perception value is built based on cumulative prospect theory. Combined with the value function of the arrival time and time value model, the cost function of psychological comprehensive perception is established, and the reference point of psychological comprehensive perception is set. Travel cumulative prospect models of bus interchanges and bus transfers to subways chosen by commuters and noncommuters are established, and the two-dimensional (departure time and travel mode) optimal travel decisions of commuters and noncommuters are obtained based on the calculation results. The results show that traveler cumulative prospect value first increases and then decreases with the delay of departure time, and the peak value’s occurrence time of a bus transfer to a subway is later than that of a bus interchange. Cumulative prospect value decreases as the transfer time increases when travelers’ arrive at the destination at the same time. Commuters obtain higher gains when they choose late departure time and bus transfer to the subway with determined transfer time, while noncommuters obtain higher gains with the opposite choice. The results show that traveler comprehensive psychological perception not only depends on arrival time but also depends on departure time, travel time in different stages, and cost. Travelers have different risk preferences for different travel purposes, and commuters’ time value is high, which determines whether they tend to pursue risk. Noncommuters tend to avoid risk. This conclusion can provide a theoretical basis for transfer decisions to improve satisfaction with public transit.
{"title":"Allowing for Psychological Comprehensive Perception Value in Transfer Decision of Public Transit","authors":"Liang Chen, Bei Tian, Shengyu Liu, Qiaoru Li","doi":"10.1061/jtepbs.0000768","DOIUrl":"https://doi.org/10.1061/jtepbs.0000768","url":null,"abstract":"To explore traveler transfer decisions for different purposes, a transfer decision model allowing psychological comprehensive perception value is built based on cumulative prospect theory. Combined with the value function of the arrival time and time value model, the cost function of psychological comprehensive perception is established, and the reference point of psychological comprehensive perception is set. Travel cumulative prospect models of bus interchanges and bus transfers to subways chosen by commuters and noncommuters are established, and the two-dimensional (departure time and travel mode) optimal travel decisions of commuters and noncommuters are obtained based on the calculation results. The results show that traveler cumulative prospect value first increases and then decreases with the delay of departure time, and the peak value’s occurrence time of a bus transfer to a subway is later than that of a bus interchange. Cumulative prospect value decreases as the transfer time increases when travelers’ arrive at the destination at the same time. Commuters obtain higher gains when they choose late departure time and bus transfer to the subway with determined transfer time, while noncommuters obtain higher gains with the opposite choice. The results show that traveler comprehensive psychological perception not only depends on arrival time but also depends on departure time, travel time in different stages, and cost. Travelers have different risk preferences for different travel purposes, and commuters’ time value is high, which determines whether they tend to pursue risk. Noncommuters tend to avoid risk. This conclusion can provide a theoretical basis for transfer decisions to improve satisfaction with public transit.","PeriodicalId":49972,"journal":{"name":"Journal of Transportation Engineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136382810","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-03-01DOI: 10.1061/jtepbs.teeng-7312
Armstrong Aboah, Yaw Adu-Gyamfi, Senem Velipasalar Gursoy, Jennifer Merickel, Matt Rizzo, Anuj Sharma
The current paper implements a methodology for automatically detecting vehicle maneuvers from vehicle telemetry data under naturalistic driving settings. Previous approaches have treated vehicle maneuver detection as a classification problem, although both time series segmentation and classification are required since input telemetry data are continuous. Our objective is to develop an end-to-end pipeline for the frame-by-frame annotation of naturalistic driving studies videos into various driving events including stop and lane-keeping events, lane changes, left-right turning movements, and horizontal curve maneuvers. To address the time series segmentation problem, the study developed an energy-maximization algorithm (EMA) capable of extracting driving events of varying durations and frequencies from continuous signal data. To reduce overfitting and false alarm rates, heuristic algorithms were used to classify events with highly variable patterns such as stops and lane-keeping. To classify segmented driving events, four machine-learning models were implemented, and their accuracy and transferability were assessed over multiple data sources. The duration of events extracted by EMA was comparable to actual events, with accuracies ranging from 59.30% (left lane change) to 85.60% (lane-keeping). Additionally, the overall accuracy of the 1D-convolutional neural network model was 98.99%, followed by the long-short-term-memory model at 97.75%, then the random forest model at 97.71%, and the support vector machine model at 97.65%. These model accuracies were consistent across different data sources. The study concludes that implementing a segmentation-classification pipeline significantly improves both the accuracy of driver maneuver detection and the transferability of shallow and deep ML models across diverse datasets.
{"title":"Driver Maneuver Detection and Analysis Using Time Series Segmentation and Classification","authors":"Armstrong Aboah, Yaw Adu-Gyamfi, Senem Velipasalar Gursoy, Jennifer Merickel, Matt Rizzo, Anuj Sharma","doi":"10.1061/jtepbs.teeng-7312","DOIUrl":"https://doi.org/10.1061/jtepbs.teeng-7312","url":null,"abstract":"The current paper implements a methodology for automatically detecting vehicle maneuvers from vehicle telemetry data under naturalistic driving settings. Previous approaches have treated vehicle maneuver detection as a classification problem, although both time series segmentation and classification are required since input telemetry data are continuous. Our objective is to develop an end-to-end pipeline for the frame-by-frame annotation of naturalistic driving studies videos into various driving events including stop and lane-keeping events, lane changes, left-right turning movements, and horizontal curve maneuvers. To address the time series segmentation problem, the study developed an energy-maximization algorithm (EMA) capable of extracting driving events of varying durations and frequencies from continuous signal data. To reduce overfitting and false alarm rates, heuristic algorithms were used to classify events with highly variable patterns such as stops and lane-keeping. To classify segmented driving events, four machine-learning models were implemented, and their accuracy and transferability were assessed over multiple data sources. The duration of events extracted by EMA was comparable to actual events, with accuracies ranging from 59.30% (left lane change) to 85.60% (lane-keeping). Additionally, the overall accuracy of the 1D-convolutional neural network model was 98.99%, followed by the long-short-term-memory model at 97.75%, then the random forest model at 97.71%, and the support vector machine model at 97.65%. These model accuracies were consistent across different data sources. The study concludes that implementing a segmentation-classification pipeline significantly improves both the accuracy of driver maneuver detection and the transferability of shallow and deep ML models across diverse datasets.","PeriodicalId":49972,"journal":{"name":"Journal of Transportation Engineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135693479","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-02-01DOI: 10.1061/jtepbs.teeng-7280
Afshin Pakzadnia, Yasser Hassan
This study evaluates different proposed merging solutions that reduce the conflict between merging vehicles and mainline traffic within a mixed traffic environment using a safety measure to see which strategy might work better than others under specific traffic conditions. The mixed traffic includes various percentages of driver-operated vehicles (DVs) and connected autonomous vehicles (CAVs). The probability of noncompliance (PNC) is selected as a surrogate safety measure to assess the strategies. A MATLAB program is developed to simulate various traffic conditions at a merging area and to calculate the PNC merging for the different merging strategies. In addition, to examine the relationship between PNC and collision frequency at the merging area, the collision data at 15 merging ramps in Ottawa were collected to examine the relationship between PNC values obtained from the simulation for the case of a full-DV vehicle fleet and no management strategy (current conditions) and actual safety performance. The results confirmed the validity of PNC as a surrogate safety measure that is correlated to expected collision frequency at merge areas. By simulating all proposed merging management strategies, the results of this study showed a general trend of decreasing PNC and, hence, improved safety performance since the CAV penetration rate increases even when no management strategy is used or under the do-nothing option. However, most merging strategies had better expected safety performance than the do-nothing option, which indicates the value of implementing a merging management strategy, especially during the period of transition from a full-DV to a full-CAV fleet.
{"title":"Expected Safety Performance of Different Freeway Merging Strategies in an Environment of Mixed Vehicle Technologies","authors":"Afshin Pakzadnia, Yasser Hassan","doi":"10.1061/jtepbs.teeng-7280","DOIUrl":"https://doi.org/10.1061/jtepbs.teeng-7280","url":null,"abstract":"This study evaluates different proposed merging solutions that reduce the conflict between merging vehicles and mainline traffic within a mixed traffic environment using a safety measure to see which strategy might work better than others under specific traffic conditions. The mixed traffic includes various percentages of driver-operated vehicles (DVs) and connected autonomous vehicles (CAVs). The probability of noncompliance (PNC) is selected as a surrogate safety measure to assess the strategies. A MATLAB program is developed to simulate various traffic conditions at a merging area and to calculate the PNC merging for the different merging strategies. In addition, to examine the relationship between PNC and collision frequency at the merging area, the collision data at 15 merging ramps in Ottawa were collected to examine the relationship between PNC values obtained from the simulation for the case of a full-DV vehicle fleet and no management strategy (current conditions) and actual safety performance. The results confirmed the validity of PNC as a surrogate safety measure that is correlated to expected collision frequency at merge areas. By simulating all proposed merging management strategies, the results of this study showed a general trend of decreasing PNC and, hence, improved safety performance since the CAV penetration rate increases even when no management strategy is used or under the do-nothing option. However, most merging strategies had better expected safety performance than the do-nothing option, which indicates the value of implementing a merging management strategy, especially during the period of transition from a full-DV to a full-CAV fleet.","PeriodicalId":49972,"journal":{"name":"Journal of Transportation Engineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136097245","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2011-07-01DOI: 10.1061/(ASCE)TE.1943-5436.0000225
Bastian J Schroeder, Nagui M Rouphail
This research explores factors associated with driver yielding behavior at unsignalized pedestrian crossings and develops predictive models for yielding using logistic regression. It considers the effect of variables describing driver attributes, pedestrian characteristics and concurrent conditions at the crosswalk on the yield response. Special consideration is given to 'vehicle dynamics constraints' that form a threshold for the potential to yield. Similarities are identified to driver reaction in response to the 'amber' indication at a signalized intersection. The logit models were developed from data collected at two unsignalized mid-block crosswalks in North Carolina. The data include 'before' and 'after' observations of two pedestrian safety treatments, an in-street pedestrian crossing sign and pedestrian-actuated in-roadway warning lights.The analysis suggests that drivers are more likely to yield to assertive pedestrians who walk briskly in their approach to the crosswalk. In turn, the yield probability is reduced with higher speeds, deceleration rates and if vehicles are traveling in platoons. The treatment effects proved to be significant and increased the propensity of drivers to yield, but their effectiveness may be dependent on whether the pedestrian activates the treatment.The results of this research provide new insights on the complex interaction of pedestrians and vehicles at unsignalized intersections and have implications for future work towards predictive models for driver yielding behavior. The developed logit models can provide the basis for representing driver yielding behavior in a microsimulation modeling environment.
{"title":"Event-Based Modeling of Driver Yielding Behavior at Unsignalized Crosswalks.","authors":"Bastian J Schroeder, Nagui M Rouphail","doi":"10.1061/(ASCE)TE.1943-5436.0000225","DOIUrl":"https://doi.org/10.1061/(ASCE)TE.1943-5436.0000225","url":null,"abstract":"<p><p>This research explores factors associated with driver yielding behavior at unsignalized pedestrian crossings and develops predictive models for yielding using logistic regression. It considers the effect of variables describing driver attributes, pedestrian characteristics and concurrent conditions at the crosswalk on the yield response. Special consideration is given to 'vehicle dynamics constraints' that form a threshold for the potential to yield. Similarities are identified to driver reaction in response to the 'amber' indication at a signalized intersection. The logit models were developed from data collected at two unsignalized mid-block crosswalks in North Carolina. The data include 'before' and 'after' observations of two pedestrian safety treatments, an in-street pedestrian crossing sign and pedestrian-actuated in-roadway warning lights.The analysis suggests that drivers are more likely to yield to assertive pedestrians who walk briskly in their approach to the crosswalk. In turn, the yield probability is reduced with higher speeds, deceleration rates and if vehicles are traveling in platoons. The treatment effects proved to be significant and increased the propensity of drivers to yield, but their effectiveness may be dependent on whether the pedestrian activates the treatment.The results of this research provide new insights on the complex interaction of pedestrians and vehicles at unsignalized intersections and have implications for future work towards predictive models for driver yielding behavior. The developed logit models can provide the basis for representing driver yielding behavior in a microsimulation modeling environment.</p>","PeriodicalId":49972,"journal":{"name":"Journal of Transportation Engineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2011-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1061/(ASCE)TE.1943-5436.0000225","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"30086490","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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