Heinrich’s Law indicates an empirical ratio between serious accidents, minor accidents, and near misses in industrial sites, but has not been discussed concerning road traffic accidents. Digital tachographs (DTG), a type of IoT device collecting spatiotemporal big data of vehicle trajectories, allows for examining a linkage between abnormal driving behaviors and the prevalence of road traffic incidents. According to the Traffic Safety Act implemented in 2011, DTG has been mandatorily pre-installed on most commercial vehicles in South Korea. The data have been analyzed to evaluate the data processing method or promote eco-driving or safe driving, but only a few studies have examined an association between driving behaviors and actual traffic accidents using the limited data. We obtained 7,785,124 DTG sensing records from 1,523 commercial taxis driving within the city limits of Seoul at least once in 2013 and integrated them with 57,139 traffic accident cases during the same period. Using the integrated GIS database, we performed a grid-based spatiotemporal mapping and analysis to calculate a ratio among abnormal driving events, minor and major traffic accidents by road type. The findings suggest a potential for enhancing road safety by monitoring and controlling abnormal driving patterns as a precursor for accidents.
More and more Connected and Autonomous Vehicle (CAV) open test roads reconstructed on the basis of traditional roads have appeared in China. However, the management policies vary, which makes the traffic environment complicated. This paper takes CAV test road safety management as the research purpose, investigates the open test condition through the evaluation of the traffic safety facilities. Indicators were rigorously screened, then the game theory model was used to determine the combination weight of the indicators, and the set pair analysis was applied to solve the uncertain problems. A case study for the CAV test road network of a city in central China was implemented and the results show that, about the traffic safety facilities’ condition, among the 20 sections of the city’s CAV test road network, 15% of which are at an excellent level, 75% of which are at a good level and 10% of which are at a moderate level; road signs, guardrail facilities, isolation facilities and road features are the main limiting factors affecting the level of traffic safety facilities. Based on the results, recommendations have been made for the transport management authorities in the aspects of the safety management policy-making and facilities maintenance.
Dockless bike-sharing (DBS) is an effective solution to the “first and last mile” problem in urban transportation. It can be integrated with urban rail transit (URT) to provide passengers with more convenient travel services. This study focuses on the integrated use of DBS and URT in Shenzhen, utilizing a multi-buffer zone approach to identify DBS data within URT station catchment areas. By employing ordinary least squares (OLS), geographically weighted regression (GWR), and multiscale geographically weighted regression (MGWR) models, the spatiotemporal heterogeneity of integrated use and its relationship with environmental factors surrounding URT stations were examined. The empirical findings highlight the superiority of the MGWR model in accurately explaining spatial relationships compared to the OLS and GWR models. Furthermore, the study reveals that the impact of built environment factors on integrated use varies during morning and evening peak periods, as well as in terms of access and egress. Specifically, factors such as catering, shopping, companies, residential buildings, bus stops, minor roads, transfer stations, and population density were found to influence the integrated use of DBS and URT. These findings not only contribute to the promotion of DBS-URT integration but also promote the overall development of urban transportation.
Türkiye's expanding population and growing economy have resulted in a significant increase in automobile ownership, leading to a rise in traffic volume and, subsequently, an increase in the number of accidents. The increase in the number of deaths and injuries caused by traffic accidents has motivated authorities and automobile manufacturers to work together to mitigate the impact of traffic accidents. Therefore, the demand for better roads, modern technologies, and higher-quality driver training is becoming increasingly urgent for traffic safety. Due to the scale of harm to the country's economy and society caused by the material and moral losses, resulting from traffic accidents, traffic safety and management is one of the most important government initiatives. One of the responsible units of traffic safety and management in Türkiye is the traffic gendarme. This study reviewed the categories of traffic accidents, the number of accidents, and the road network that occurred in a province's gendarme responsibility area in Türkiye and linked them with the number of traffic gendarmes in that province. Thus, the study utilized Mixed Integer Programming based on Multi-Criteria Decision Making methods to identify the areas where these traffic gendarmes will be deployed according to established principles.
This study investigated several factors that may influence driver actions throughout the yellow interval at urban signalized intersections. The selected samples include 2168 observations. Almost 33% of drivers stopped ahead of the stop line, 60% passed the intersection through the yellow interval, and 7% passed after the yellow interval was complete (Red Light Running, RLR violations). Binary logistic regression models showed that the chance of passing went up as vehicle speed went up and down as the gap between the vehicle and the traffic light and green interval went up. The movement type and vehicle position influenced the passing probability, but the vehicle type did not. Moreover, multinomial logistic regression models showed that the legal passing probability declined with the growth in the green time and vehicle distance to the traffic signal. It also increased with the growth in the speed of approaching vehicles. Also, movement type directly affected the chance of legally passing, but vehicle position and type did not. Furthermore, the driver's performance during the yellow phase was studied using k-nearest neighbors algorithm (KNN), Support vector machines (SVM), Random Forest (RF), and AdaBoost machine learning techniques. The driver’s action run prediction was the most accurate, and the run-on-red camera was the least accurate.
Parking search reduces the quality of parking service, as well as traffic network level of service, due to additionally generated traffic. Parking search also entails other negative effects, primarily ecological, social and economic. Even though the importance of this problem has been noted in the past, there is an impression that this issue has not been sufficiently researched and should be additionally analyzed in order to properly understand this phenomenon. Therefore, the aim of this paper is to study the factors affecting parking search time that can be influenced through a set of parking management measures. In this paper, an ordinal regression model was developed to estimate these parameters and it was fitted using empirical data collected by interviewing drivers. Main model results show that parking occupancy has the highest impact upon the value of parking search time, indicating the significance of defining proper policies and measures aimed at reaching targeted parking occupancy. Parking frequency is the second parameter observed to be significant, demonstrating the importance of implementing proper parking information systems.
Road elements are increasingly digitalized to provide drivers advanced assistance especially in the emergent or adverse conditions. It is challenging and expensive to accurately digitalize all the road elements especially on the urban roads with many infrastructures and complex designs, where we may focus on the most important ones at the first stage. This research designs a questionnaire to ask the drivers to rank the importance of the road elements in various driving conditions. Driver characteristics are also collected, including age, driving style, accident experience, and accumulated driving distance, to explore their effect on drivers’ cognition of road elements importance. It is found that driving is a complex activity, and the moving elements (e.g. surrounding cars) are more important than the non-moving ones. Attention should be paid to the road elements even distant from the ego car, to get prepared to the potential driving risk or penalty. Statistical difference between the experienced and non-experienced drivers recommends that driver assistance system should be sufficiently trained in various conditions, to build up autonomous driving tactics and skills. This research promotes the understanding of driving cognition pattern to provide insights into the development of road digitalization.
In this paper, the influence of traffic flow volume and meteorological conditions on carbon monoxide (CO), particulate matter (PM), ozone (O3) and sulfur dioxide (SO2) concertation is determined based on the measurements conducted at a selected location over a 784-hour period. Multivariate Analysis of Variance (MANOVA) and Analysis of Variance (ANOVA) were applied to the data set. Regression analysis was also used to determine trends in pollutant concentrations as a function of traffic flow and meteorological parameters. Analysis of the obtained data indicates a statistically significant relationship between traffic volume and meteorological parameters on the one hand and pollutants on the other. However, increase in the value of certain input variables does not necessarily result in the increase in pollutant concentration. CO and O3 showed a significant dependence on the number of vehicles, while for SO2 the influence of commercial vehicles was greater than that of passenger cars. The relationship between the number of vehicles and PM was not evident at the study site.
First, the exploratory factor analysis was used to identify the initial questionnaire's underlying structure, including a set of 67 items in 13 domains. Next, confirmatory factor analysis was undertaken to assess the questionnaire's reliability, discriminant validity and goodness of fit. CFA produced a proper fit with adequate discriminant validity and internal consistency. CFA and Cronbach’s alpha results in the final version of the RLRBCQ consisted of 39 items assessing 13 domains, explaining 69.799% of the variance, and internal consistency reliability values ranging from 0.710 to 0.825. These results suggest that the RLRBCQ demonstrates reliable, stable, and valid properties, which can be used to assess potential determinants of avoiding red light running behavior following the domains of the TDF. And it can be utilized by safety managers and practitioners to guide the design of interventions for various traffic safety behaviors.
The article discusses the results of studies of railway line capacity relative to the application of additional block division using virtual blocks in the process of positioning of a train reporting its position and train set integrity. The studies were conducted using the authors’ original simulation software enabling extensive parameterisation of infrastructure, including configuration of the train control system and signalling principles, by taking the actual characteristics of train movements into account based on data obtained from real-life measurements.
Effectively equilibrating passenger distribution on metro platforms and carriages is important for relieving local congestion. This paper explores the role of incentive mechanisms in encouraging passengers’ queuing behaviours. To quantitatively analyse passengers' compliance with the policy, a questionnaire survey was conducted in Fuzhou, China. According to the preliminary analysis of the survey data, passengers have various moving distance preferences under the incentive scenarios, namely, no movement, less distance and greater distance. Additionally, this paper establishes a nested logit model that considers travel purposes and moving distances. The empirical results show that although monetary and point-system incentives can effectively enhance passengers’ compliance with transfer queue-positioning requirements, when the moving distance is very small, people pay less attention to rewards. Compared to those commuting on weekends, passengers commuting on weekdays comply with policies more strongly, and the effect of implementing incentive policies is better; however, the effect of these policies is reduced among those travelling for leisure. Meanwhile, when travelling for leisure, as the number of companions increases, people's willingness to follow the guidance on where to wait increases. According to the results, the implementation of incentive-based waiting encouragement policies during peak working days can result in good compliance.
At present,interest in the application of unmanned aerial vehicles (UAV) for the delivery is growing. A new "multi-type of UAV collaborative delivery" mode has been proposed. Through a combination of large, medium and small UAVs, the delivery capabilities of the UAV logistics system are significantly improved. Sometimes there's high demand, resulting in planned delivery routes that are no longer feasible, and even cause a shortage of distribution centre capacity and drones.This study explores logistics delivery strategies to solve problems caused by high demand.In this study, a multitype and multidistribution UAV model was established with the objective of minimising the total cost of distribution by considering factors such as the UAV energy consumption, load, and distribution centre conditions. An improved ant colony algorithm was designed, and its effectiveness was verified through the variability of the calculation time and multiple calculation results of different-scale examples. Finally, the classic vehicle routing problem (VRP) case is used in three scenarios to analyse the UAV scheduling optimisation problem. The results indicate that assisted delivery can reduce costs by 3%, while ensuring delivery timeliness. The results of this study can provide guidance and benchmarks for the application of UAVs in urban logistics delivery systems.
The main objective of the transport reliability and maintenance analysis is to improve the understanding of accidents through incident investigations. This research focuses on composite pneumatic tyres used in transportation engineering and presents both theoretical and experimental studies. The finite element method used for numerical simulation combined with pre-experimental measurements based on optimisation by material vibration response is presented for tyre material modelling. Piezoelectric vibration test was used for the pre-experimental test of the tyre quarters. The simulation results indicate that the pneumatic tyre with the recommended air pressure inflation shows the least amount of deformation. In comparison, pneumatic tyres with recommended and reduced air pressure inflation of 0.25, 0.5 and 0.75 bar are under research. Additionally, it was established that, when subjected to external forces that exceed the tyre’s maximum weight capacity, as determined by the manufacturer, the tyre exhibits significant stiffening and internal stress. The research suggests that this methodology can be used to obtain a realistic model of vehicle tyre dynamic processes and assess the impact on road traffic safety with different inflation pressures and loads.
The structural deficiencies of the terminal delivery path often make it the main culprit of urban traffic congestion and environmental pollution. Traditional studies of express networks regarded them as an independent entity, ignoring the endogenous role of urban road network morphology and structure. To solve this problem, this paper explored the spatial dependency of terminal delivery routes in Xi'an City based on the idea of bipartite graph network. A spatial dependency matrix of delivery paths-urban roads was constructed by abstracting delivery paths as node-set A and urban roads as node-set B. And three spatial dependencies indexes including degree centrality, betweenness centrality and closeness centrality were introduced to analyze the coupling features of these two objects. Results show that these dependency measures can reflect the coupling features of urban terminal delivery paths and urban roads. First, degree centrality demonstrates terminal delivery path coverage and coupling hierarchy and scale-free nature. Second, betweenness centrality presents the road utilization balance of terminal delivery paths. Third, closeness centrality explains how easy it is for delivery paths to connect with others.
Connected and autonomous vehicles (CAVs) are recognized as a technology trend in the transportation engineering arena. As one of the most popular capabilities of CAVs, trajectory planning attracts extensive attention and interest from both academia and the industry. Segmentized trajectory planning is gaining popularity for its simplicity and robustness in computation and deployment. Constructive recommendations and guidelines can be provided by exploring the effects of segmentized trajectories in different settings of CAVs and intersections. This research proposes a control strategy for segmentized trajectory planning in fixed signal timing environment. To test the effects of this control strategy, this research designs simplified fixed signalized intersection scenarios and implements segmentized trajectory planning features of CAVs with different traffic demand scenarios, distances, and speed limits. The results show that the proposed control strategy has stable superior performances in different traffic scenarios especially when the traffic volume is near capacity.
Based on two-sided market theory, this paper has studied pricing problem of ride-hailing platforms with combination of inter-group network externality and inner-group network externality. Two scenarios of user structure are considered. In scenario 1, both traveler and driver single-homing. In scenario 2, traveler single-homing while driver multi-homing. Moreover, factors of time sensitive and proportion of driver’s commission rate are introduced to reflect the characteristic of transport industry. Finally, the impact of network externality, time sensitive factor, proportion of driver’s commission rate and entry cost on the ride-hailing platform’s pricing, user scale and the profit are analyzed. The results show that, the inter-group network externality and inner-group network externality has a negative effect on the platform’s price that charged to both travelers and drivers. But when travelers are multi-homing, the price charged to travelers is positive to the inter-group network externality from drivers. The relationship between travelers’ scale and inter-group network externality, inner-group network externality is positive. Further, in both scenarios, the network externalities from two sides affect platform’s profit negatively.
The role of transportation is becoming increasingly important in the world economy, and road transport in particular plays a very important role in all types of transportation. For this reason, it is extremely important that its performance is monitored regularly. Very often, this is done using Data Envelopment Analysis (DEA) performance evaluation models, and consequently, there are numerous articles in the literature on DEA evaluation of road transport systems. In this study, we first summarize these articles and classify them according to different characteristics (environmental, safety, economic, energy). Finally, we use them as a basis for developing a novel DEA framework, which is used for the evaluation of the efficiency and ranking of road transport systems that also takes into account undesirable outputs, i.e., environmental and safety outputs. As a case study, we evaluate 28 European countries from technical, safety, and environmental aspects. The CCR and SBM models are used to evaluate the efficiency of these countries for the last two years of published data. The results show that Denmark ranks first and Cyprus last for both years. It could also be found that safety efficiency is generally rated lower than other criteria. Finally, the results and reasons for the efficiency and inefficiency of specific decision-making units, i.e., countries, are discussed.
Tram signal priority control is a crucial approach for enhancing the reliability of tram operations and has been implemented in various cities. Nevertheless, unpredictable tram operation influenced by tram dwell time during station stops can cause signal priority control failure at intersections. It is challenging to precisely predict tram dwell time at stations that offer multiple lines. To address this issue, the proposed research presents a capped robust optimal control (CRC) technique for tram signal priority. This method entails considering the stochastic number of passengers boarding and alighting at stations with multiple lines. Furthermore, tram delay calculation models at intersections are established and integrated into an objective function. The main objective of this strategy is to enhance tram operation reliability and maximizing tram operation efficiency while reducing the adverse impact of tram priority on other vehicles at the intersection. A case study was conducted to evaluate the effectiveness of the CRC method. The results indicate that the CRC technique significantly improves tram operation reliability and efficiency.
This article deals with the highly topical issue of greening air transport chains. It is important to consider the environmental aspect of the current performance of air transport in regions with less intensive air transport chains, such as the Eastern Adriatic. The regional airports of Ljubljana, Zagreb and Belgrade are dependent on European air freight hubs and at the same time have the task of connecting the national airports in Sarajevo, Podgorica, etc., which complicates the functioning of air transport chains regionally. A comprehensive consideration of air freight chains is important in terms of price and transport time, but also in terms of GHG footprint. The results show that an environmental assessment of air transport chains is necessary for a more comprehensive decision on sustainable supply chains. The study enriches the scientific understanding of air transport chains in the Eastern Adriatic region from the point of view of carbon footprint and energy efficiency of transport and highlights the need to use already developed IT tools in the assessment and modelling of transport chains when different options are presented to cargo owners. Integrating the above approaches and data into current business models enables the gradual regional decarbonisation of air transport.
Autonomous Vehicles (AVs) and human-driven vehicles (HDVs) will share the roads for a long time, hence the need to study traffic flows mixing AVs and HDVs, especially during the AV introduction period. This paper aims to investigate the roadway and traffic characteristics that affect the impact of AVs on freeway traffic operations, using an adapted version of the HCM6 truck passenger-car equivalent (PCE) methodology. A large number of scenarios comprising different roadway characteristics, AV types, and traffic flow compositions were simulated using Vissim to obtain AV PCEs. The results indicated that, for all scenarios considered, an AV has a 20% lower impact on the quality of service and operation than an HDV. A CART decision tree indicated that the most important factors affecting the AVs’ impact on traffic operations are vehicle-to-vehicle connectivity level and the capability of traveling in platoons. Maximum platoon length did not matter, and the increase in the number of traffic lanes reduced the positive impact of AVs on service quality.
With the popularity of electric vehicles, they have become an indispensable part of traffic flow on the road network. This paper presents a reliability-based network equilibrium model to realize the traffic flow pattern prediction on the road network with electric vehicles and gasoline vehicles, which incorporates the travel time reliability, electric vehicles’ driving range, and recharge requirement. The mathematical expression of reliable path travel time is derived, and the reliability-based network equilibrium model is formulated as a variational inequality problem. Then a multi-criterion labeling algorithm is proposed to solve the reliable shortest path problem, and a column-generation-based method of the successive average algorithm is proposed to solve the reliability-based network equilibrium model. The applicability and efficiency of the proposed model and algorithm are verified on the Nguyen-Dupuis network and the real road network of Sioux Falls City. The proposed model and algorithm can be extended to other road networks and help traffic managers analyze traffic conditions and make sustainable traffic policies.
This paper focuses on daily freight train scheduling and dynamic railcar flow transportation problems for rail freight transportation at the operational level. Two mixed integer linear programming models that adopted different strategies were formulated based on a continuous two-layer time-space network. We simultaneously considered the benefits of railway transportation enterprise and service quality when setting the objective function. By solving the models, we can distribute the dynamic railcar flows to the train paths in the basic train timetable to obtain the daily train operation plan over a short time horizon (e.g., a day), which will be helpful for dispatchers to make decisions such as the empty railcar distribution, car routing (trip planning). Finally, we compared two models on a part of the Chinese railway network. The results show that the second model can effectively improve the efficiency of railway freight transportation.
Logistics playing a significant role in supporting economic growth and material security during the epidemic period, is experiencing rapid development in recent years. With the issues of personalization and cost, the economy and society ask for higher requirements for logistics storage systems. The rational design of the functional area layout is an essential step to improve the operational efficiency of the logistics warehousing system. In reality, due to warehouse design and equipment application, there has been a gradual increase in irregular warehouses. We take an irregular warehouse as an example, combining the operation status quo, this paper clarifies the functional area settings, and constructs a 0-1 integer planning model based on the grid and systematic layout planning method with constraints, such as the unique functional attributes of the grid. We optimize the genetic algorithm based on the warehouse irregularity factor and the grids factor, and then solve it through MATLAB. Finally, using Flexsim software, simulation metrics were selected for evaluation, the method feasibility is verified.
Non-synchronized timetables of the first hour trains can lead to longer waiting times for passengers wishing to transfer at the transfer station. This study aims to reduce the waiting time of passengers by synchronizing the timetables of first hour trains using actual transfer times. The transfer times of the passengers are obtained from the observations and are used in this synchronization study. The genetic and simulated annealing algorithms are implemented to solve the first train synchronization model. Finally, a case study is conducted on a section of the Istanbul metro network to test the synchronization model. The results show that the total waiting time of the first hour trains transfer passengers is reduced by 35% by applying the proposed model. Another result of the study shows that using the actual transfer time instead of the average transfer time of the passengers reduces the average waiting time of the passengers by 19%.
Aiming at two aircraft conflict scenario in the pre-tactical stage, by converting the uncertain flight trajectory of the target aircraft into a spatio-temporal trajectory under its performance constraints, a conflict detection model based on truncated normal distribution was proposed, and influencing factors affecting the overall conflict probability were analyzed by numerical simulation. For the conflict scenario, nonlinear particle swarm optimization (NPSO) algorithm was applied to solve the optimal separation configuration strategy for the ownship. The simulation results show that, in comparison to conventional PSO algorithm, the improved NPSO algorithm improves the optimal value by 14.88% and decreases the maximum velocity change by 19.84%. The simulation also shows that the algorithm can maintain the minimum interval requirements under different initial parameters, demonstrating its strong adaptability.