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Electric two-wheeled vehicle is one of the main commuting tools in China, but they are also more likely to have violations of the road group. In order to study the effect of the presence of violation on the severity of road traffic crashes among electric two-wheeler riders, in this study, the effects of rider characteristics, road characteristics, collision characteristics, and environmental characteristics on the severity of injuries of electric two-wheeled vehicle riders were considered separately analyzed based on the data of 6403 two-wheeled electric vehicle traffic crashes in a region of Shandong Province from 2015 to 2021, and a random parametric logit model considering the heterogeneity of the mean and the variance (RP-HMV logit) was established based on the presence or absence of violation behaviors of riders, respectively, in order to explore unobserved heterogeneity. In order to test the validity of the model for modeling the injury severity of pedestrians riding electric two-wheelers, multinomial logit (MN-logit model), and random parameter logit model (RP-logit) were estimated, and the results showed that the RP-HMV logit model was significantly superior in terms of goodness of fit. The study showed that some of the factors differed somewhat between the two scenarios, such as gender, while the factors that were significant in both scenarios were >60, broken pavement, street lights at night, no street lights at night, mixed motorized and nonmotorized lanes, sidewalks, other angles, no control, severe weather, and visibility, Author(s): Fulu Wei [1]; Yuxin Han [1]; Yanyong Guo (corresponding author) [2]; Yongqing Guo [1] 1. Introduction In recent years, electric two-wheeled vehicles have become one of the main means [...]

Traffic accident data‐based portrait plays a vital role in accident cause investigation, relationship reasoning, prevention, and control. The traffic accident data tend to be multisourced with increasingly hidden and complicated accident relationships. The existing reported research focus more on traffic drivers' measurement of penalty, the relationship among drivers, cars, and dates, etc. How to use multisource data based on deep learning, especially based on the Chinese recent unstructured data and structured data to establish accident portrait for individual and groups of accident drivers, still lacks. Moreover, how to perform multisource accident data label extraction, identity, and relationship extraction are still challenging problems. This paper proposes a multisource accident datasets‐driven deep learning‐based traffic accident portrait method. Our multisource accident datasets‐driven deep learning model is composed of the following three submodels: (1) the structured data accident model using our accident feature‐driven bidirectional long short‐term memory (Bi‐LSTM) and accident feature‐driven bidirectional conditional random field (Bi‐CRF) model to extract labels, (2) the unstructured traffic accident data model using our accident feature‐driven piecewise convolutional neural network (PCNN) model to identify the extracted labels, and (3) the semistructured traffic accident data processing model. Moreover, to solve the problem of how to construct hidden relationship among the multisource accident data, a multisource accident data visualization method based on traffic accident knowledge graph where the accident relational inference algorithm is to complete the hidden relationship between traffic accident data labels is used and then data are visualized using the traffic accident knowledge graph. This paper uses the NER dataset of the People's Daily and a manually labeled dataset to test the Bi‐LSTM + Bi‐CRF model, and it acquires the highest scores of 0.9562 and 0.9779 compared with several other models. This paper uses the DuIE dataset and a manually labeled dataset to test the PCNN model, and it acquires the highest scores of 0.9674 and 0.9108 compared with several other models. Experiments verified our model's merits than other models in regards to accident label extraction, accident identity identification, and accident relationship extraction. [ABSTRACT FROM AUTHOR]

This study aims to explore the relationship between traffic flow states and crash type/severity in the scenarios of normal crashes, primary crashes, and secondary crashes using the association rules mining approach. The crash data and real‐time traffic data were collected from the I‐880 freeway for five years in California, USA. The secondary crashes were identified using a speed contour plot approach. Traffic flow states were identified by the three‐phase flow theory. The results showed that the free flow is associated with the proportion of the sideswipe normal crash, the hit object primary crash, and the injury primary crash. The synchronized flow, the wide moving jams, and the transitional state from synchronized flow to wide moving jams are associated with the proportion of the rear‐end secondary crash. The transitional state from synchronized flow to free flow is associated with the proportion of the rear‐end primary crash and the property damage only primary crash. In addition, the unsafe speed behaviour can increase the proportion of the rear‐end normal, primary, and secondary crashes. The unsafe lane change behaviour can increase the proportion of the sideswipe normal, primary, and secondary crashes. These results have the potential to reduce the secondary crash probability. [ABSTRACT FROM AUTHOR]

In this work, a two-echelon location-routing problem with time windows and transportation resource sharing (2E-LRPTWTRS) is solved by selecting facility locations and optimizing two-echelon vehicle routes. The optimal solutions improve the efficiency of a logistics network based on the geographical distribution and service time windows of logistics facilities and customers. Furthermore, resource utilization is maximized by enabling resource sharing strategies within and among different logistics facilities simultaneously. The 2E-LRPTWTRS is formulated as a biobjective optimization model, and obtaining the smallest number of required delivery vehicles and the minimum total operating cost are the two objective functions. A two-stage hybrid algorithm composed of k-means clustering and extended multiobjective particle swarm optimization algorithm is proposed for 2E-LRPTWTRS optimization. A self-adaptive mechanism of flight parameters is introduced and adopted during the iterative process to balance the evolution of particles and improve the efficiency of the two-stage hybrid algorithm. Moreover, 20 small-scale instances are used for an algorithm comparison with multiobjective genetic algorithm and nondominated sorting genetic algorithm-II, and the solutions demonstrate the superiority of the proposed algorithm in optimizing logistics networks. The proposed optimization model and hybrid algorithm are tested by employing a real-world case of 2E-LRPTWTRS in Chongqing, China, and the optimization results verify the positive role of the developed model and algorithm in improving logistics efficiency, reducing operating cost, and saving transportation resources in the operations of two-echelon logistics networks., Author(s): Yong Wang [1]; Yaoyao Sun [1]; Xiangyang Guan [2]; Yanyong Guo (corresponding author) [3] 1. Introduction For decades, effective modern supply chain management has played an important, positive role [...]

This study aims to evaluate the effect of ultra-low emission zone (ULEZ) on the demand of public bike sharing in London (also known as London Cycle Hire Scheme (LCH)). Bike usage data from 699 bicycle docking stations between May 2019 and October 2019 are used. Treatment and control groups for effectiveness evaluation of ULEZ are established using the propensity score matching approach, with which the confounding effects by factors including population density, socio-demographics, land use, and transport facilities are considered. Results indicate that bicycle demand increases significantly (27.9%) after the introduction of ULEZ. In addition, increases in bicycle demand are more profound for short (with 15 minutes, 25.3%) and intermediate (15 to 30 minutes, 28.8%) trips. Furthermore, increase in bicycle trip with origin (borrowing) and destination (return) both within ULEZ is considerable (44.8%). However, there is no significant change for overall bicycle trip duration after the introduction of ULEZ. [ABSTRACT FROM AUTHOR]

The objective of this study is to evaluate the safety impacts of unconventional outside left-turn lane at signalized intersections. New designed unconventional outside left-turn lanes are increasingly used at signalized intersections in urban areas in China. The unconventional outside left-turn lane design allows an exclusive left-turn lane to be located to the right of through lanes to improve the efficiency and increase the capacity of left-turn movements. However, the design also raises some concerns regarding potential negative safety impacts. The evaluation is conducted using an automated video-based traffic conflict technique. The traffic conflicts approach provides better understanding of collision contributing factors and the failure mechanism that leads to road collisions. Traffic conflicts are automatically detected and time to collision is calculated based on the analysis of the vehicles' positions in space and time. Video data are collected from a signalized intersection in Nanjing, China, where both traditional inside and unconventional outside left-turn lanes are installed on two intersection approaches. The other two approaches have only inside left-turn lanes. The study compared frequency and severity of conflict for left-turning vehicles as well as the percentage of vehicles involved in conflicts from the inside and outside left-turn lanes. The results show that the intersection approaches with outside left-turn lanes had considerably more conflicts compared to approaches without outside left-turn lanes. As well, the approaches with outside left-turn lanes had significantly higher conflict severity than the approaches without outside left-turn lanes. As such, it is recommended that the trade-off between the improved mobility and negative safety impact of outside left-turn lanes be carefully considered before recommending their installation. Key words: safety analysis, traffic conflicts, computer vision, outside left- turn lanes. Resume : L'objectif de cette etude est d'evaluer les repercussions sur la securite des voies tourne-a-gauche exterieures non conventionnelles aux carrefours a feux. On effectue l'evaluation a l'aide des techniques de conflit de circulation automatisees a partir d'enregistrements video. Les conflits de circulation sont automatiquement detectes et le temps avant la collision est calcule en fonction de l'analyse des positions des vehicules dans l'espace et le temps. On a recueilli des donnees video provenant d'un carrefour a feux a Nanjing en Chine, oU a la fois des voies traditionnelles interieures ainsi que des voies non conventionnelles tourne-a-gauche exterieures sont installees a deux approches de carrefour. L'etude a compare la frequence et la gravite des conflits des vehicules tournant a gauche ainsi que le pourcentage de vehicules impliques dans des conflits dans les voies de tourne-a-gauche interieures et exterieures. Les resultats montrent que les approches de carrefour ayant des voies de tourne-a-gauche exterieures avaient plus de conflits et une gravite de conflit plus elevee comparees aux approches sans voies de tourne-a-gauche exterieures. On recommande de soigneusement considerer le choix entre l'amelioration de la mobilite et les effets negatifs des voies tourne-a-gauche exterieures sur la securite avant de recommander leur installation. [Traduit par la Redaction] Mots-cles : analyse de la securite, conflits de circulation, vision artificielle, voies tourne-a-gauche exterieures., Introduction China has seen significant increase in private vehicle ownership over the past two decades, which has resulted in high levels of traffic congestion. In many large cities in China, [...]

The primary objective of this study is to evaluate factors affecting e-bike involved crash and license plate use in China. E-bike crashes data were collected from police database and completed through a telephone interview. Noncrash samples were collected by a questionnaire survey. A bivariate probit (BP) model was developed to simultaneously examine the significant factors associated with e-bike involved crash and e-bike license plate and to account for the correlations between them. Marginal effects for contributory factors were calculated to quantify their impacts on the outcomes. The results show that several contributory factors, including gender, age, education level, driver license, car in household, experiences in using e-bike, law compliance, and aggressive driving behaviors, are found to have significant impacts on both e-bike involved crash and license plate use. Moreover, type of e-bike, frequency of using e-bike, impulse behavior, degree of riding experience, and risk perception scale are found to be associated with e-bike involved crash. It is also found that e-bike involved crash and e-bike license plate use are strongly correlated and are negative in direction. The result enhanced our comprehension of the factors related to e-bike involved crash and e-bike license plate use., 1. Introduction In the last decades, there has been a rapid increase in the use of electric bicycles (e-bikes) in China. E-bikes provide users with convenient, flexible, and affordable mobility. [...]