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Network capacity, defined as the largest sum of origin–destination (O–D) flows that can be accommodated by the network based on link performance function and traffic equilibrium assignment, is a critical indicator of network-wide performance assessment in transportation planning and management. The typical modeling rationale of estimating network capacity is to formulate it as a mathematical programming (MP), and there are two main approaches: single-level MP formulation and bi-level programming (BLP) formulation. Although single-level MP is readily solvable, it treats the transportation network as a physical network without considering level of service (LOS). Albeit BLP explicitly models the capacity and link LOS, solving BLP in large-scale networks is challenging due to its non-convexity. Moreover, the inconsideration of trip LOS makes the existing models difficult to differentiate network capacity under various traffic states and to capture the impact of emerging trip-oriented technologies. Therefore, this paper proposes the α-max capacity model to estimate the maximum network capacity under trip or O–D LOS requirement α. The proposed model improves the existing models on three aspects: (a) it considers trip LOS, which can flexibly estimate the network capacity ranging from zero to the physical capacity including reserve, practical and ultimate capacities; (b) trip LOS can intuitively reflect users’ maximum acceptable O–D travel time or planners’ requirement of O–D travel time; and (c) it is a convex and tractable single-level MP. For practical use, we develop a modified gradient projection solution algorithm with soft constraint technique, and provide methods to obtain discrete trip LOS and network capacity under representative traffic states. Numerical examples are presented to demonstrate the features of the proposed model as well as the solution algorithm.
Zhaoqi Zang; Xiangdong Xu; Anthony Chen; Chao Yang. Modeling the α-max capacity of transportation networks: a single-level mathematical programming formulation. Transportation 2021, 1 -33.
AMA StyleZhaoqi Zang, Xiangdong Xu, Anthony Chen, Chao Yang. Modeling the α-max capacity of transportation networks: a single-level mathematical programming formulation. Transportation. 2021; ():1-33.
Chicago/Turabian StyleZhaoqi Zang; Xiangdong Xu; Anthony Chen; Chao Yang. 2021. "Modeling the α-max capacity of transportation networks: a single-level mathematical programming formulation." Transportation , no. : 1-33.
Due to the burgeoning demand for freight movement, freight related road safety threats have been growing substantially. In spite of some research on the factors influencing freight truck-related crashes in major cities, the literature offers limited evidence about the effects of the built environment on the occurrence of those crashes by injury severity. This article uses data from the Los Angeles region in 2010–2019 to explore the relationships between the built environment factors and the spatial distribution of freight truck-related crashes using XGBoost and SHAP methods. Results from the XGBoost model show that variables related to the built environment, in particular demographics, land uses and road network, are highly correlated to freight truck related crashes of all three injury types. The SHAP value plots further indicate the important nonlinear relationships between independent variables and dependent variables. This study also emphasizes the differences in modeling mechanisms between the XGBoost model and traditional statistical models. The findings will help transport planners develop operational measures for resolving the emerging freight truck related traffic safety problems in local communities.
Chao Yang; Mingyang Chen; Quan Yuan. The application of XGBoost and SHAP to examining the factors in freight truck-related crashes: An exploratory analysis. Accident Analysis & Prevention 2021, 158, 106153 .
AMA StyleChao Yang, Mingyang Chen, Quan Yuan. The application of XGBoost and SHAP to examining the factors in freight truck-related crashes: An exploratory analysis. Accident Analysis & Prevention. 2021; 158 ():106153.
Chicago/Turabian StyleChao Yang; Mingyang Chen; Quan Yuan. 2021. "The application of XGBoost and SHAP to examining the factors in freight truck-related crashes: An exploratory analysis." Accident Analysis & Prevention 158, no. : 106153.
Pattern clustering is an effective method for exploring the regularities of human mobility scheduling and daily activities. There still remains the challenge of measuring the similarity between pairs of activity patterns that are in the form of categorical time series sequences. Existing studies measured similarity using binary vector or edit distance, but these methods were insufficient to characterize routine arrangement and time scheduling of daily activities. To address this issue, we cluster daily activities and identify regular patterns using a Markov-chain-based mixture model, which captures features of activity scheduling by Markov transition matrix as well as measures similarity with probability distribution. Logistic regression models are further built to test hypothetical relationships between activity patterns and socio-demographic characteristics. Results show there are three main human activity patterns in terms of daily routine arrangement and activity scheduling: working-education-oriented (WE-oriented), recreation-shopping-oriented (RS-oriented), and schooling-drop-off/pick-up-oriented (SDP-oriented). People in the WE-oriented pattern mainly engage with regular home-based commuting trips, while people in the RS-oriented pattern are involved in home-based shopping and entertainment events. With regard to the SDP-oriented pattern, people plan their trips under a restricted scheduling of schooling pickup/drop-off. Each pattern clearly indicates long-term regularity of daily activity behaviors and corresponds to specific socio-demographics. Distinguishing three categories of residents with distinct life styles, this research would help accommodate travel demand from different groups of people in urban transportation planning.
Yang Zhou; Quan Yuan; Chao Yang; Yinhai Wang. Who you are determines how you travel: Clustering human activity patterns with a Markov-chain-based mixture model. Travel Behaviour and Society 2021, 24, 102 -112.
AMA StyleYang Zhou, Quan Yuan, Chao Yang, Yinhai Wang. Who you are determines how you travel: Clustering human activity patterns with a Markov-chain-based mixture model. Travel Behaviour and Society. 2021; 24 ():102-112.
Chicago/Turabian StyleYang Zhou; Quan Yuan; Chao Yang; Yinhai Wang. 2021. "Who you are determines how you travel: Clustering human activity patterns with a Markov-chain-based mixture model." Travel Behaviour and Society 24, no. : 102-112.
Due to the burgeoning demand for freight movement in the era of e-commerce, freight related road safety threats have been growing in both urban and suburban areas, despite the improved general traffic safety over the past decades. The empirical evidence on how freight trucks related crashes are distributed across neighborhoods and correlated to spatially varying factors is, however, highly limited. This article uses data from the Los Angeles region in 2018 to analyze the spatial patterns of freight trucks related traffic crashes and examines the major factors that contribute to those patterns using spatial econometric models. Maps show that freight trucks related crashes are highly associated with major freight generators but less clustered than the overall traffic crashes. Results from the spatial Durbin model indicate that access to freight generators, economic attributes, land uses, road infrastructure, and road network variables all contribute to the spatial distribution of freight trucks related crashes. The findings could help transport planners understand the dynamics of freight trucks related traffic safety and develop operational measures for mitigating the impacts of growing goods movement on local communities.
Chao Yang; Mingyang Chen; Quan Yuan. The geography of freight-related accidents in the era of E-commerce: Evidence from the Los Angeles metropolitan area. Journal of Transport Geography 2021, 92, 102989 .
AMA StyleChao Yang, Mingyang Chen, Quan Yuan. The geography of freight-related accidents in the era of E-commerce: Evidence from the Los Angeles metropolitan area. Journal of Transport Geography. 2021; 92 ():102989.
Chicago/Turabian StyleChao Yang; Mingyang Chen; Quan Yuan. 2021. "The geography of freight-related accidents in the era of E-commerce: Evidence from the Los Angeles metropolitan area." Journal of Transport Geography 92, no. : 102989.
The rapid aging of the population has posed significant challenges to society and raised new demand for transportation services. Understanding travel needs of the elderly is crucial to making effective strategies for accommodating their demand in many newly motorized cities in developing countries such as China. Using a Markov-chain-based mixture model, we identify two main activity patterns of the elderly: recreation-shopping-oriented (RS-oriented) pattern and schooling-drop-off/pick-up-oriented (SDP-oriented) pattern. Elderly people in the RS-oriented pattern enjoy a cozy life with much time spent on recreation and shopping activities, while those in the SDP-oriented pattern take responsibility of sending grandchildren to school and taking them back home. The RS-oriented elderly people are faced with spatial constraints to access the sparsely distributed recreational sights; however, the SDP-oriented group is subject to temporal constraints when making daily trips. These results would encourage policy makers to reconsider the role of transportation in aged people’s lives and better accommodate their demand through designing safer walking and cycling environment and improving the quality of transit services.
Yang Zhou; Quan Yuan; Chao Yang. Transport for the Elderly: Activity Patterns, Mode Choices, and Spatiotemporal Constraints. Sustainability 2020, 12, 10024 .
AMA StyleYang Zhou, Quan Yuan, Chao Yang. Transport for the Elderly: Activity Patterns, Mode Choices, and Spatiotemporal Constraints. Sustainability. 2020; 12 (23):10024.
Chicago/Turabian StyleYang Zhou; Quan Yuan; Chao Yang. 2020. "Transport for the Elderly: Activity Patterns, Mode Choices, and Spatiotemporal Constraints." Sustainability 12, no. 23: 10024.
Smartphones have been advocated as the preferred devices for travel behavior studies over conventional surveys. But the primary challenges are candidate stops extraction from GPS data and trip ends distinction from noise. This paper develops a Resident Travel Survey System (RTSS) for GPS data collection and travel diary verification, and then uses a two-step method to identify trip ends. In the first step, a density-based spatio-temporal clustering algorithm is proposed to extract candidate stops from trajectories. In the second step, a random forest model is applied to distinguish trip ends from mode transfer points. Results show that the clustering algorithm achieves a precision of 96.2%, a recall of 99.6%, mean absolute error of time within 3 min, and average offset distance within 30 meters. The comprehensive accuracy of trip ends identification is 99.2%. The two-step method performs well in trip ends identification and promotes the efficiency of travel survey systems.
Yang Zhou; Chao Yang; Rongrong Zhu. Identifying trip ends from raw GPS data with a hybrid spatio-temporal clustering algorithm and random forest model: a case study in Shanghai. Transportation Planning and Technology 2019, 42, 739 -756.
AMA StyleYang Zhou, Chao Yang, Rongrong Zhu. Identifying trip ends from raw GPS data with a hybrid spatio-temporal clustering algorithm and random forest model: a case study in Shanghai. Transportation Planning and Technology. 2019; 42 (8):739-756.
Chicago/Turabian StyleYang Zhou; Chao Yang; Rongrong Zhu. 2019. "Identifying trip ends from raw GPS data with a hybrid spatio-temporal clustering algorithm and random forest model: a case study in Shanghai." Transportation Planning and Technology 42, no. 8: 739-756.
Travel time reliability (TTR) has received great attention in the past decades. The majority of TTR measures rely on the travel time percentile function as a basic element for performance evaluation. There are two main approaches for deriving the travel time percentile function: simple unimodal probability distribution models and mixture/nonparametric models. Despite the tractability of the former approach, they cannot sufficiently capture the travel time distributions (TTDs) due to their heterogeneity, and also often encounters many issues such as the failure of significance tests and the indecisiveness among multiple fitted distributions. On the other hand, the latter approach possesses greater flexibility for capturing diverse TTDs, but it does not have a simple and closed-form travel time percentile function. Motivated by the above drawbacks, this paper proposes a closed-form and flexible approach for estimating the travel time percentile function of diverse TTDs based on the Cornish–Fisher expansion without the need to assume/fit a certain distribution type. To ensure a high-quality estimation, we introduce and integrate two improvements with theoretically proven foundation into the Cornish–Fisher expansion while guaranteeing a closed-form expression of the travel time percentile function. Specifically, the first improvement, logarithm transformation, increases the probability of satisfying the validity domain of the Cornish–Fisher expansion; while the second improvement, rearrangement, guarantees a monotone travel time percentile function when travel time datasets cannot satisfy the validity domain after the logarithm transformation. Realistic travel time datasets are used to examine the accuracy and robustness of the proposed method. Compared to five widely-used probability distributions, the proposed method is sufficiently adaptable to estimating percentile function of diverse TTDs with lower estimation error. More importantly, it has a closed-form expression of the travel time percentile function, which would facilitate characterizing TTR in large-scale network applications.
Zhaoqi Zang; Xiangdong Xu; Chao Yang; Anthony Chen. A closed-form estimation of the travel time percentile function for characterizing travel time reliability. Transportation Research Part B: Methodological 2018, 118, 228 -247.
AMA StyleZhaoqi Zang, Xiangdong Xu, Chao Yang, Anthony Chen. A closed-form estimation of the travel time percentile function for characterizing travel time reliability. Transportation Research Part B: Methodological. 2018; 118 ():228-247.
Chicago/Turabian StyleZhaoqi Zang; Xiangdong Xu; Chao Yang; Anthony Chen. 2018. "A closed-form estimation of the travel time percentile function for characterizing travel time reliability." Transportation Research Part B: Methodological 118, no. : 228-247.
A popular phenomenon in the street-hailing taxi system is the imbalanced mobility services between city central and outside downtown areas, which leads to unmet demand outside downtown areas and competitions in city central areas. Understanding taxi drivers’ customer-searching behaviors is crucial to addressing the phenomenon and redistributing the taxi supply. However, the current literature ignores or simply models the taxi drivers’ behaviors, in particular, lacks the in-depth discussions on individuals’ heterogeneity. This study introduces the latent class model to identify the internal and external factors influencing the taxi drivers’ destination choice after the last drop-offs. Beyond the influencing factors, the modeling structure captures the heterogeneity in vacant taxicab drivers through introducing latent classes. The proposed model outperforms other discrete choice models, for instance, multinomial logit, nested logit, and mixed logit, based on the two study cases developed from the New York City yellow taxicab system. The empirical results first statistically indicate the existence of latent classes, which further empirically prove the heterogeneity in the choices by vacant taxicab drivers while searching customers. Moreover, we obtain a set of internal and external factors influencing the customer searching behaviors. For example, the taxicab drivers are sensitive to the demand at the search destination areas and the distance from the last drop-off location to the search destination areas and behave identically in particular under the conditions of high demand and short search distance. On the other hand, the external variables have different impacts on customer searching behaviors across the different groups of drivers in the both study cases, including peak hours, weekday, holiday, earned fare from last occupied trip, raining hours, and flight arrivals at airports. In final, the proposed modeling structure and findings are useful as a sub-model of taxi system modeling while developing strategies, as well as as a regional planning tool for taxi supply estimations.
Wenbo Zhang; Satish V. Ukkusuri; Chao Yang. Modeling the Taxi Drivers’ Customer-Searching Behaviors outside Downtown Areas. Sustainability 2018, 10, 3003 .
AMA StyleWenbo Zhang, Satish V. Ukkusuri, Chao Yang. Modeling the Taxi Drivers’ Customer-Searching Behaviors outside Downtown Areas. Sustainability. 2018; 10 (9):3003.
Chicago/Turabian StyleWenbo Zhang; Satish V. Ukkusuri; Chao Yang. 2018. "Modeling the Taxi Drivers’ Customer-Searching Behaviors outside Downtown Areas." Sustainability 10, no. 9: 3003.
The Network Design Problem (NDP) is a strategical decision-making problem in planning, designing, and managing road networks with the aim to make efficient use of limited resources for optimizing the road network performance. Sustainability development is a major concern of various social-economic systems throughout the world. As a critical component of sustainable development, transportation systems should be designed to make positive contributions to the economic, environmental, and social sustainability of the served regions and communities. This requirement significantly uplifts the challenges on the modeling and the analysis of NDP. In this paper, we provide a review on the sustainable road NDP. Specifically, an overview on the three dimensions of sustainable development (i.e., economic, environmental, and social) is first provided, focusing on their representative performance measures relevant to road NDP. Then, we review the existing studies with the classification system of economy and environmentoriented sustainable NDP, economy and equity-oriented sustainable NDP, and three-dimensional sustainable NDP. Future research directions are suggested for advancing the methodological advancement and practical applications of sustainable transportation NDP.
Xiangdong Xu; Anthony Chen; Chao Yang. A review of sustainable network design for road networks. KSCE Journal of Civil Engineering 2016, 20, 1084 -1098.
AMA StyleXiangdong Xu, Anthony Chen, Chao Yang. A review of sustainable network design for road networks. KSCE Journal of Civil Engineering. 2016; 20 (3):1084-1098.
Chicago/Turabian StyleXiangdong Xu; Anthony Chen; Chao Yang. 2016. "A review of sustainable network design for road networks." KSCE Journal of Civil Engineering 20, no. 3: 1084-1098.
Urban vehicle emission models have been utilized to calculate pollutant concentrations at both microscopic and macroscopic levels based on vehicle emission rates which few researches have been able to validate. The objective of our research is to estimate urban roadside emissions and calibrate it with in-field measurement data. We calculated the vehicle emissions based on localized emission rates, and used an atmospheric dispersion model to estimate roadside emissions. A non-linear regression model was applied to calibrate the localized emission rates using in-field measurement data. With the calibrated emission rates, emissions on urban roadside can be estimated with a high accuracy.
Yichao Pu; Chao Yang. Estimating urban roadside emissions with an atmospheric dispersion model based on in-field measurements. Environmental Pollution 2014, 192, 300 -307.
AMA StyleYichao Pu, Chao Yang. Estimating urban roadside emissions with an atmospheric dispersion model based on in-field measurements. Environmental Pollution. 2014; 192 ():300-307.
Chicago/Turabian StyleYichao Pu; Chao Yang. 2014. "Estimating urban roadside emissions with an atmospheric dispersion model based on in-field measurements." Environmental Pollution 192, no. : 300-307.
Microscopic emission models are widely used in emission estimation and environment evaluation. Traditionally, microscopic traffic simulation models and probe vehicles are two sources of inputs to a microscopic emission model. However, they are not effective in reflecting all vehicles' real‐world operating conditions. Using each vehicle's spot speed data recorded by detectors, this paper provides a new method to estimate all vehicles' real‐world activities data. These data can then be used as inputs to a microscopic emission model to estimate vehicle fuel consumption and emissions. The main task is to reconstruct trajectory of each vehicle and calculate second‐by‐second speed and acceleration from the activities data. The Next Generation Simulation dataset and the Comprehensive Modal Emissions Model are used in this study to calculate and analyze the emission results for both lane‐level and link‐level. The results showed that using the proposed method for estimating vehicle fuel consumption and emissions is promising. Copyright © 2012 John Wiley & Sons, Ltd.
Zhong Chen; Chao Yang; Anthony Chen. Estimating fuel consumption and emissions based on reconstructed vehicle trajectories. Journal of Advanced Transportation 2012, 48, 627 -641.
AMA StyleZhong Chen, Chao Yang, Anthony Chen. Estimating fuel consumption and emissions based on reconstructed vehicle trajectories. Journal of Advanced Transportation. 2012; 48 (6):627-641.
Chicago/Turabian StyleZhong Chen; Chao Yang; Anthony Chen. 2012. "Estimating fuel consumption and emissions based on reconstructed vehicle trajectories." Journal of Advanced Transportation 48, no. 6: 627-641.