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 aim and 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, regarding 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 safety management policy-making and facilities maintenance.
Turkey’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 Turkey 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 Turkey and linked them with the number of traffic gendarmes in that province. Thus, the study utilised 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.
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.
Heinrich’s Law indicates an empirical ratio between serious accidents, minor accidents and near misses in industrial sites, but has not been discussed in the context of road traffic accidents. Digital tachographs (DTG), a type of IoT device collecting spatiotemporal big data of vehicle trajectories, allow0 for examining a linkage between abnormal driving behaviours 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 analysed to evaluate the data processing method or promote eco-driving or safe driving, but only a few studies have examined an association between driving behaviours 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.
This study investigated several factors that may influence driver actions throughout the yellow interval at urban signalised intersections. The selected samples include 2,168 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 the k-nearest neighbours 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.
To develop effective interventions to transit the instant delivery service riders towards avoiding red light running behaviour, a valid and reliable questionnaire is needed to identify the potential theoretical factors that influence the intention. This study describes the development and validation of the red light running behaviour causes questionnaire based on the theoretical domains framework. 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 behaviour following the domains of the TDF. It can be utilised by safety managers and practitioners to guide the design of interventions for various traffic safety behaviours.
This paper presents the main challenges of integrating micromobility vehicles into modern traffic and transportation systems. Although micromobility seems to be an effective concept for the first and last mile, the reality points to the potential problems that the integration of micromobility vehicles can create and that must be resolved appropriately. Micromobility vehicles are characterised by extensive development, which is not accompanied by appropriate legal regulations. The street design has its spatial limitations and usually separates non-motorised and motorised users, which is a notion that could be disrupted by new micromobility options. When it comes to Serbia, the existing legislation does not recognise the majority of micromobility vehicles, which results in the lack of safety of these participants and their place in the street profile. The aim of this paper is to provide guidelines for improving the existing regulations and integrating these vehicles into the traffic system of Serbia, with special reference to general recommendations through which micromobility vehicles can be treated in other countries. The results of this paper can be useful to decision-makers but also to all other participants in the process of developing effective policies and strategies for the integration of micromobility vehicles into traffic and transportation systems.
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, utilising 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 the DBS-URT integration but also promote the overall development of urban transportation.
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 analysed 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.
Freight transport significantly contributes to urban traffic but is often overlooked by decision-makers compared to passenger transport. Conventional transportation modelling studies often use aggregate approaches for freight transport, undoubtedly due to the difficulty of data collection. However, the nature of freight transportation is much more complex. For this reason, examining the determinants of freight vehicle preferences with discrete approaches is crucial for the contributions that can be put forward, especially in local studies. To address this apparent gap in the study of local disaggregated approaches to freight transport, we present a discrete modelling-based methodology to investigate the factors that determine freight vehicle preferences for shippers and senders. The estimated nested logit model is constructed with the RU2 approach, the second part of random utility theory, thus avoiding the theoretical inconsistencies that arise when generic coefficients are used. As a result, the model structures provided satisfactory results compared to the literature. It was revealed that the factors affecting freight vehicle choice preferences were influenced by packaging preferences and differed according to freight groups. This local study is the first nested logit study for freight modelling in Istanbul and it is aimed to shed light on future national studies.
In this paper, the influence of traffic flow volume and meteorological conditions on carbon monoxide (CO), particulate matter (PM), ozone (O3) and sulphur 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.
The emergence of global health crises has emphasised the need for efficient and secure methods to verify the individuals’ vaccination and testing status. The research paper proposes an innovative application of the Radio Frequency Identification technology to develop a comprehensive system that ensures the controlled entry of vaccinated and tested individuals into buildings and extends its use to transportation networks. By combining tags, readers and a centralised database, with the extension of using Message Queuing Telemetry Transport, this solution aims to enhance public health safety, restore confidence in public spaces and optimise transportation operations.