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Bei Zhou
College of Transportation Engineering, Chang’an University, Xi’an 710064, China

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Research article
Published: 28 January 2021 in Journal of Advanced Transportation
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We investigate the dynamic performance of traffic flow using a modified optimal velocity car-following model. In the car-following scenarios, the following vehicle must continuously adjust the following distance to the preceding vehicle in real time. A new optimal velocity function incorporating the desired safety distance instead of a preset constant is presented first to describe the abovementioned car-following behavior dynamically. The boundary conditions of the new optimal velocity function are theoretically analyzed. Subsequently, we propose an improved car-following model by combining the heterogeneity of driver’s sensitivity based on the new optimal velocity function and previous car-following model. The stability criterion of the improved model is obtained through the linear analysis method. Finally, numerical simulation is performed to explore the effect of the desired safety distance and the heterogeneity of driver’s sensitivity on the traffic flow. Results show that the proposed model has considerable effects on improving traffic stability and suppressing traffic congestion. Furthermore, the proposed model is compatible with the heterogeneity of driver’s sensitivity and can enhance the average velocity of traffic flow compared with the conventional model. In conclusion, the dynamic performance of traffic flow can be improved by considering the desired safety distance and the heterogeneity of driver’s sensitivity in the car-following model.

ACS Style

Lei Zhang; Shengrui Zhang; Bei Zhou; Shuaiyang Jiao; Yan Huang. An Improved Car-Following Model considering Desired Safety Distance and Heterogeneity of Driver’s Sensitivity. Journal of Advanced Transportation 2021, 2021, 1 -12.

AMA Style

Lei Zhang, Shengrui Zhang, Bei Zhou, Shuaiyang Jiao, Yan Huang. An Improved Car-Following Model considering Desired Safety Distance and Heterogeneity of Driver’s Sensitivity. Journal of Advanced Transportation. 2021; 2021 ():1-12.

Chicago/Turabian Style

Lei Zhang; Shengrui Zhang; Bei Zhou; Shuaiyang Jiao; Yan Huang. 2021. "An Improved Car-Following Model considering Desired Safety Distance and Heterogeneity of Driver’s Sensitivity." Journal of Advanced Transportation 2021, no. : 1-12.

Journal article
Published: 20 August 2020 in IEEE Access
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This study aims to explore factors affecting passenger car and truck driver injury severity in passenger car-truck crashes. Police-reported crash data from 2007 to 2017 in Canada are collected. Two-vehicle crashes involving one truck and one passenger car are extracted for modeling. Different injury severities are not equally represented. To address the data imbalance issue, this study applies four different data imbalance treatment approaches, including over-sampling, under-sampling, a hybrid method, and a cost-sensitive learning method. To test the performances of different classifiers, five classification models are used, including multinomial logistic regression, Naive Bayes, Classification and Regression Tree, support vector machine, and eXtreme Gradient Boosting (XGBoost). In both the passenger car driver and truck driver injury severity analysis, XGBoost combined with cost-sensitive learning generates the best results in terms of G-mean, area under the curve, and overall accuracy. Additionally, the Shapley Additive Explanations (SHAP) approach is adopted to interpret the result of the best-performing model. Most of the explanatory variables have similar effects on passenger car and truck driver fatality risks. Nevertheless, six variables exhibit opposite effects, including the age of the passenger car driver, crash hour, the passenger car age, road surface condition, weather condition and the truck age. Results of this study could provide some valuable insights for improving truck traffic safety. For instance, properly installing traffic control devices could be an effective way to reduce fatality risks in passenger car-truck crashes. Besides, passenger car drivers should be extremely cautious when driving between midnight to 6 am on truck corridors.

ACS Style

Bei Zhou; Xiqing Wang; Shengrui Zhang; Zongzhi Li; Shaofeng Sun; Kun Shu; Qing Sun. Comparing Factors Affecting Injury Severity of Passenger Car and Truck Drivers. IEEE Access 2020, 8, 153849 -153861.

AMA Style

Bei Zhou, Xiqing Wang, Shengrui Zhang, Zongzhi Li, Shaofeng Sun, Kun Shu, Qing Sun. Comparing Factors Affecting Injury Severity of Passenger Car and Truck Drivers. IEEE Access. 2020; 8 (99):153849-153861.

Chicago/Turabian Style

Bei Zhou; Xiqing Wang; Shengrui Zhang; Zongzhi Li; Shaofeng Sun; Kun Shu; Qing Sun. 2020. "Comparing Factors Affecting Injury Severity of Passenger Car and Truck Drivers." IEEE Access 8, no. 99: 153849-153861.

Journal article
Published: 25 March 2020 in Sustainability
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To promote the sustainable development and safety of bicycle traffic, survival analysis of the risk perception sensitivity of cyclists is proposed. The cumulative probability of survival serves as an index of risk perception sensitivity, and a Cox regression model is established. The proposed method is applied to middle school cyclists, and the factors of their risk perception are analyzed. Data are collected by questionnaire and traffic conflict survey and are quantified by factor analysis. The model results show that active and extroverted personality, negative peer influence, unsafe riding behavior intention, non-motor vehicle flow and speed, and a lack of separation facilities have negative correlations with risk perception sensitivity. Positive attitude towards traffic rules, good family education, heightened traffic safety awareness, motor vehicle flow and speed, pedestrian flow, and non-motorized lane width have positive correlations with risk perception sensitivity. The conflict type has no correlation with risk perception sensitivity. This study aims to improve the sensitivity of risk perception, prevent traffic conflicts and provide a theoretical basis for risk perception research on vulnerable traffic participants.

ACS Style

Dan Zhao; Shengrui Zhang; Bei Zhou; Shuaiyang Jiao; Ling Yang. Risk Perception Sensitivity of Cyclists Based on the Cox Risk Perception Model. Sustainability 2020, 12, 2613 .

AMA Style

Dan Zhao, Shengrui Zhang, Bei Zhou, Shuaiyang Jiao, Ling Yang. Risk Perception Sensitivity of Cyclists Based on the Cox Risk Perception Model. Sustainability. 2020; 12 (7):2613.

Chicago/Turabian Style

Dan Zhao; Shengrui Zhang; Bei Zhou; Shuaiyang Jiao; Ling Yang. 2020. "Risk Perception Sensitivity of Cyclists Based on the Cox Risk Perception Model." Sustainability 12, no. 7: 2613.

Journal article
Published: 19 February 2020 in Sustainability
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In intelligent transportation systems, vehicles can obtain more information, and the interactivity between vehicles can be improved. Therefore, it is necessary to study car-following behavior during the introduction of intelligent traffic information technology. To study the impacts of drivers’ characteristics on the dynamic characteristics of car-following behavior in a vehicle-to-vehicle (V2V) communication environment, we first analyzed the relationship between drivers’ characteristics and the following car’s optimal velocity using vehicle trajectory data via the grey relational analysis method and then presented a new optimal velocity function (OVF). The boundary conditions of the new OVF were analyzed theoretically, and the results showed that the new OVF can better describe drivers’ characteristics than the traditional OVF. Subsequently, we proposed an extended car-following model by combining V2V communication based on the new OVF and previous car-following models. Finally, numerical simulations were carried out to explore the effect of drivers’ characteristics on car-following behavior and fuel economy of vehicles, and the results indicated that the proposed model can improve vehicles’ mobility, safety, fuel consumption, and emissions in different traffic scenarios. In conclusion, the performance of traffic flow was improved by taking drivers’ characteristics into account under the V2V communication situation for car-following theory.

ACS Style

Shuaiyang Jiao; Shengrui Zhang; Bei Zhou; Zixuan Zhang; Liyuan Xue. An Extended Car-Following Model Considering the Drivers’ Characteristics under a V2V Communication Environment. Sustainability 2020, 12, 1552 .

AMA Style

Shuaiyang Jiao, Shengrui Zhang, Bei Zhou, Zixuan Zhang, Liyuan Xue. An Extended Car-Following Model Considering the Drivers’ Characteristics under a V2V Communication Environment. Sustainability. 2020; 12 (4):1552.

Chicago/Turabian Style

Shuaiyang Jiao; Shengrui Zhang; Bei Zhou; Zixuan Zhang; Liyuan Xue. 2020. "An Extended Car-Following Model Considering the Drivers’ Characteristics under a V2V Communication Environment." Sustainability 12, no. 4: 1552.

Research article
Published: 14 January 2020 in Journal of Advanced Transportation
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This paper introduces an improved car-following speed (CFS) model that simultaneously considers speed of the lead vehicle, vehicle spacing, and driver’s sensitivity to them. Specifically, the proposed model extends the Helbing-Tilch model and Yang et al. model developed based on the principle of grey relational analysis where vehicle spacing is considered as the primary factor contributing to car-following speed choices. A computational experiment is conducted for model calibration using vehicle spacing, speed, and acceleration data derived from vehicle trajectory data of the Next Generation Simulation (NGSIM) project sponsored by the Federal Highway Administration (FHWA). It shows that speed of the lead vehicle and vehicle spacing significantly affect speed of the lag vehicle. Further, model validation is carried out using an independent NGSIM dataset by comparing vehicle speed predictions made by the calibrated CFS model with Helbing-Tilch model and Yang et al. model as benchmarks. Compared with speed prediction results of the benchmark models, mean relative errors, root mean square errors, and equal coefficient of speed predictions of the CFS model have reduced by 72.41% and 61.85%, 70.14% and 57.99%, and 33.15% and 14.48%, respectively. The findings of model validation reveal that the CFS model could improve the accuracy of speed predictions in the car-following process.

ACS Style

Shuaiyang Jiao; Shengrui Zhang; Zongzhi Li; Bei Zhou; Dan Zhao. An Improved Car-Following Speed Model considering Speed of the Lead Vehicle, Vehicle Spacing, and Driver’s Sensitivity to Them. Journal of Advanced Transportation 2020, 2020, 1 -13.

AMA Style

Shuaiyang Jiao, Shengrui Zhang, Zongzhi Li, Bei Zhou, Dan Zhao. An Improved Car-Following Speed Model considering Speed of the Lead Vehicle, Vehicle Spacing, and Driver’s Sensitivity to Them. Journal of Advanced Transportation. 2020; 2020 ():1-13.

Chicago/Turabian Style

Shuaiyang Jiao; Shengrui Zhang; Zongzhi Li; Bei Zhou; Dan Zhao. 2020. "An Improved Car-Following Speed Model considering Speed of the Lead Vehicle, Vehicle Spacing, and Driver’s Sensitivity to Them." Journal of Advanced Transportation 2020, no. : 1-13.

Journal article
Published: 17 June 2019 in Sustainability
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The popular real-time ridesharing service has promoted social and environmental sustainability in various ways. Meanwhile, it also brings some traffic safety concerns. This paper aims to analyze factors affecting real-time ridesharing vehicle crash severity based on the classification and regression tree (CART) model. The Chicago police-reported crash data from January to December 2018 is collected. Crash severity in the original dataset is highly imbalanced: only 60 out of 2624 crashes are severe injury crashes. To fix the data imbalance problem, a hybrid data preprocessing approach which combines the over- and under-sampling is applied. Model results indicate that, by resampling the crash data, the successfully predicted severe crashes are increased from 0 to 40. Besides, the G-mean is increased from 0% to 73%, and the AUC (area under the receiver operating characteristics curve) is increased from 0.73 to 0.82. The classification tree reveals that following variables are the primary indicators of real-time ridesharing vehicle crash severity: pedestrian/pedalcyclist involvement, number of passengers, weather condition, trafficway type, vehicle manufacture year, traffic control device, driver gender, lighting condition, vehicle type, driver age and crash time. The current study could provide some valuable insights for the sustainable development of real-time ridesharing services and urban transportation.

ACS Style

Bei Zhou; Xinfen Zhang; Shengrui Zhang; Zongzhi Li; Xin Liu. Analysis of Factors Affecting Real-Time Ridesharing Vehicle Crash Severity. Sustainability 2019, 11, 3334 .

AMA Style

Bei Zhou, Xinfen Zhang, Shengrui Zhang, Zongzhi Li, Xin Liu. Analysis of Factors Affecting Real-Time Ridesharing Vehicle Crash Severity. Sustainability. 2019; 11 (12):3334.

Chicago/Turabian Style

Bei Zhou; Xinfen Zhang; Shengrui Zhang; Zongzhi Li; Xin Liu. 2019. "Analysis of Factors Affecting Real-Time Ridesharing Vehicle Crash Severity." Sustainability 11, no. 12: 3334.

Journal article
Published: 04 March 2019 in Sustainability
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Hit-and-run (HR) crashes refer to crashes involving drivers of the offending vehicle fleeing incident scenes without aiding the possible victims or informing authorities for emergency medical services. This paper aims at identifying significant predictors of HR and non-hit-and-run (NHR) in vehicle-bicycle crashes based on the classification and regression tree (CART) method. An oversampling technique is applied to deal with the data imbalance problem, where the number of minority instances (HR crash) is much lower than that of the majority instances (NHR crash). The police-reported data within City of Chicago from September 2017 to August 2018 is collected. The G-mean (geometric mean) is used to evaluate the classification performance. Results indicate that, compared with original CART model, the G-mean of CART model incorporating data imbalance treatment is increased from 23% to 61% by 171%. The decision tree reveals that the following five variables play the most important roles in classifying HR and NHR in vehicle-bicycle crashes: Driver age, bicyclist safety equipment, driver action, trafficway type, and gender of drivers. Several countermeasures are recommended accordingly. The current study demonstrates that, by incorporating data imbalance treatment, the CART method could provide much more robust classification results.

ACS Style

Bei Zhou; Zongzhi Li; Shengrui Zhang; Xinfen Zhang; Xin Liu; Qiannan Ma. Analysis of Factors Affecting Hit-and-Run and Non-Hit-and-Run in Vehicle-Bicycle Crashes: A Non-Parametric Approach Incorporating Data Imbalance Treatment. Sustainability 2019, 11, 1327 .

AMA Style

Bei Zhou, Zongzhi Li, Shengrui Zhang, Xinfen Zhang, Xin Liu, Qiannan Ma. Analysis of Factors Affecting Hit-and-Run and Non-Hit-and-Run in Vehicle-Bicycle Crashes: A Non-Parametric Approach Incorporating Data Imbalance Treatment. Sustainability. 2019; 11 (5):1327.

Chicago/Turabian Style

Bei Zhou; Zongzhi Li; Shengrui Zhang; Xinfen Zhang; Xin Liu; Qiannan Ma. 2019. "Analysis of Factors Affecting Hit-and-Run and Non-Hit-and-Run in Vehicle-Bicycle Crashes: A Non-Parametric Approach Incorporating Data Imbalance Treatment." Sustainability 11, no. 5: 1327.

Research article
Published: 04 November 2018 in Journal of Advanced Transportation
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A hit-and-run (HR) crash occurs when the driver of the offending vehicle flees the crash scene without reporting it or aiding the victims. The current study aimed at contributing to existing literatures by comparing factors which might affect the crash severity in HR and non-hit-and-run (NHR) crashes. The data was extracted from the police-reported crash data from September 2017 to August 2018 within the City of Chicago. Two multinomial logistic regression models were established for the HR and NHR crash data, respectively. The odds ratio (OR) of each variable was used to quantify the impact of this variable on the crash severity. In both models, the property damage only (PDO) crash was selected as the reference group, and the injury and fatal crash were chosen as the comparison group. When the injury crash was taken as the comparison group, it was found that 12 variables contributed to the crash severities in both HR and NHR model. The average percentage deviation of OR for these 12 variables was 34%, indicating that compared with property damage, HR crashes were 34% more likely to result in injuries than NHR crashes on average. When fatal crashes were chosen as the comparison group, 2 variables were found to be statistically significant in both the HR and the NHR model. The average percentage deviation of OR for these 2 variables was 127%, indicating that compared with property damage, HR crashes were 127% more likely to result in fatalities than NHR crashes on average.

ACS Style

Bei Zhou; Zongzhi Li; Shengrui Zhang. Comparison of Factors Affecting Crash Severities in Hit-and-Run and Non-Hit-and-Run Crashes. Journal of Advanced Transportation 2018, 2018, 1 -11.

AMA Style

Bei Zhou, Zongzhi Li, Shengrui Zhang. Comparison of Factors Affecting Crash Severities in Hit-and-Run and Non-Hit-and-Run Crashes. Journal of Advanced Transportation. 2018; 2018 ():1-11.

Chicago/Turabian Style

Bei Zhou; Zongzhi Li; Shengrui Zhang. 2018. "Comparison of Factors Affecting Crash Severities in Hit-and-Run and Non-Hit-and-Run Crashes." Journal of Advanced Transportation 2018, no. : 1-11.

Corrigendum
Published: 19 August 2018 in Mathematical Problems in Engineering
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ACS Style

Bei Zhou; Arash M. Roshandeh; Shengrui Zhang. Corrigendum to “Safety Impacts of Push-Button and Countdown Timer on Nonmotorized Traffic at Intersections”. Mathematical Problems in Engineering 2018, 2018, 1 -1.

AMA Style

Bei Zhou, Arash M. Roshandeh, Shengrui Zhang. Corrigendum to “Safety Impacts of Push-Button and Countdown Timer on Nonmotorized Traffic at Intersections”. Mathematical Problems in Engineering. 2018; 2018 ():1-1.

Chicago/Turabian Style

Bei Zhou; Arash M. Roshandeh; Shengrui Zhang. 2018. "Corrigendum to “Safety Impacts of Push-Button and Countdown Timer on Nonmotorized Traffic at Intersections”." Mathematical Problems in Engineering 2018, no. : 1-1.

Journal article
Published: 01 April 2016 in Transportation Research Part F: Traffic Psychology and Behaviour
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Among different types of crashes, hit-and-run is driver’s failure to stop after a vehicle crash. There are many accidents where drivers could actually be at fault or totally innocent, and leaving the scene would turn an innocent driver into a criminal. The current paper aims to contribute to the literature by exploring the association of different variables pertaining to the condition of infrastructure, environment, driver, population of the area, and crash severity and type with hit-and-run crashes. The analysis is performed for two data sets: (i) crashes where the driver was distracted; and (ii) crashes where driver was not distracted. Hit-and-run crash data with corresponding factors are police-reported data for crashes within Cook County, Illinois, occurring between 2004 and 2012. A logistic regression model assessed 43 variables within 16 categories for statistically significant association with hit-and-run crashes, for drivers with and without distraction. For both driver distraction statuses, 17 variables were associated with a significant increased probability of a hit-and-run crash and 10 variables were associated with a significant decreased probability. Additionally, it was found that crashes on curve level and curve hillcrest road alignment types were associated with increased likelihood of a hit-and-run crash when the driver was distracted and decreased likelihood when the driver was not distracted. Variables related to hit-and-run crashes vary depending on driver’s distraction status. When comparing likelihood to flee the scene after a crash, non-distracted drivers are 27% less likely to do so compared to distracted drivers.

ACS Style

Arash M. Roshandeh; Bei Zhou; Ali Behnood. Comparison of contributing factors in hit-and-run crashes with distracted and non-distracted drivers. Transportation Research Part F: Traffic Psychology and Behaviour 2016, 38, 22 -28.

AMA Style

Arash M. Roshandeh, Bei Zhou, Ali Behnood. Comparison of contributing factors in hit-and-run crashes with distracted and non-distracted drivers. Transportation Research Part F: Traffic Psychology and Behaviour. 2016; 38 ():22-28.

Chicago/Turabian Style

Arash M. Roshandeh; Bei Zhou; Ali Behnood. 2016. "Comparison of contributing factors in hit-and-run crashes with distracted and non-distracted drivers." Transportation Research Part F: Traffic Psychology and Behaviour 38, no. : 22-28.

Journal article
Published: 01 May 2014 in Journal of Transportation Engineering
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ACS Style

Bei Zhou; Zongzhi Li; Harshingar Patel; Arash M. Roshandeh; Yuanqing Wang. Risk-Based Two-Step Optimization Model for Highway Transportation Investment Decision-Making. Journal of Transportation Engineering 2014, 140, 4014007 .

AMA Style

Bei Zhou, Zongzhi Li, Harshingar Patel, Arash M. Roshandeh, Yuanqing Wang. Risk-Based Two-Step Optimization Model for Highway Transportation Investment Decision-Making. Journal of Transportation Engineering. 2014; 140 (5):4014007.

Chicago/Turabian Style

Bei Zhou; Zongzhi Li; Harshingar Patel; Arash M. Roshandeh; Yuanqing Wang. 2014. "Risk-Based Two-Step Optimization Model for Highway Transportation Investment Decision-Making." Journal of Transportation Engineering 140, no. 5: 4014007.

Journal article
Published: 01 May 2014 in Journal of Transportation Engineering
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ACS Style

Arash M. Roshandeh; Herbert S. Levinson; Zongzhi Li; Harshingar Patel; Bei Zhou. New Methodology for Intersection Signal Timing Optimization to Simultaneously Minimize Vehicle and Pedestrian Delays. Journal of Transportation Engineering 2014, 140, 04014009 .

AMA Style

Arash M. Roshandeh, Herbert S. Levinson, Zongzhi Li, Harshingar Patel, Bei Zhou. New Methodology for Intersection Signal Timing Optimization to Simultaneously Minimize Vehicle and Pedestrian Delays. Journal of Transportation Engineering. 2014; 140 (5):04014009.

Chicago/Turabian Style

Arash M. Roshandeh; Herbert S. Levinson; Zongzhi Li; Harshingar Patel; Bei Zhou. 2014. "New Methodology for Intersection Signal Timing Optimization to Simultaneously Minimize Vehicle and Pedestrian Delays." Journal of Transportation Engineering 140, no. 5: 04014009.

Journal article
Published: 01 July 2013 in Journal of Transportation Engineering
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A new methodology is proposed to address issues of networkwide effects of highway projects and interdependencies of jointly implementing multiple projects to support optimal investment decisions. Specifically, a multicommodity minimum cost network (MMCN) model is introduced to help obtain link-based traffic volumes, vehicle compositions, and speeds for a highway network that could be used for project benefit estimation using life cycle cost analysis approaches. A hypergraph knapsack model is formulated to identify the best subcollection of interdependent projects to achieve maximized overall benefits for a given budget level. A computational study is conducted using the proposed methodology for tollway capital investment decision making. It is revealed that the overall project benefits with risk and uncertainty considerations for facility construction and treatment costs, traffic forecasts, and the discount rate are significantly lower than the estimated project benefits without risk and uncertainty considerations. In addition, project interdependencies are found to exist for all joint project implementation scenarios. With the continuing growth of budget levels, the overall benefits of projects selected for implementation using the hypergraph knapsack could reach a maximum, beyond which no extra benefits could be obtained. The methodology may be adopted by state and local transportation agencies and metropolitan planning organizations to enhance investment decisions.

ACS Style

Zongzhi Li; Arash M. Roshandeh; Bei Zhou; Sang Hyuk Lee. Optimal Decision Making of Interdependent Tollway Capital Investments Incorporating Risk and Uncertainty. Journal of Transportation Engineering 2013, 139, 686 -696.

AMA Style

Zongzhi Li, Arash M. Roshandeh, Bei Zhou, Sang Hyuk Lee. Optimal Decision Making of Interdependent Tollway Capital Investments Incorporating Risk and Uncertainty. Journal of Transportation Engineering. 2013; 139 (7):686-696.

Chicago/Turabian Style

Zongzhi Li; Arash M. Roshandeh; Bei Zhou; Sang Hyuk Lee. 2013. "Optimal Decision Making of Interdependent Tollway Capital Investments Incorporating Risk and Uncertainty." Journal of Transportation Engineering 139, no. 7: 686-696.