When roundabouts face congestion problems, the transition to signalised roundabouts is considered a solution to the problem. The majority of studies have concentrated on how to calculate the optimal cycle length and signal timing to minimise congestion at roundabouts. To date, intelligence algorithms with multi-objectives such as queue length, number of stops, delay time, capacity and so on are widely used for calculating signal timing. Although roundabout congestion can be generated by the weaving zone reducing roundabout capacity, there have been minimal studies which take into account the density in the weaving zone. This study proposed a hybrid gravitational search algorithm – ABFO random forest regression with the following objectives: density, delay time and capacity to find the optimal cycle length and green time in each phase of Changwon city hall roundabout in South Korea as a case study. The optimal cycle length and green time were calculated in MATLAB and microscopic simulation VISSIM sought the effectiveness of a signalised roundabout. The result of the analysis demonstrated that signalised roundabouts with 102 seconds cycle length (phase 1 – 65 seconds of green time and phase 2 – 37 seconds of green time) can reduce density by 46.1%, delays by 32.8% and increase roundabout capacity by 14.8%.

Vehicle exhausts diffuse into roadside crowd breathing zones, thereby jeopardising human health. This study applies dynamic traffic distribution theories to comprehensively consider the impact of vehicle emission diffusion. The results provide a theoretical basis for improving the diffusion of urban traffic pollution to benefit the surrounding environment for roadside crowds. Firstly, a multi-vehicle cellular transport model that is suitable for analysing dynamic traffic distribution is constructed considering the distinct emission factors of various types of vehicles. Secondly, a multi-vehicle emission model is established to consider a range of driving conditions. Then, the concept of roadside crowd exposure risk is introduced, and we describe a method for calculating the total amounts of pollutants emitted by vehicles and inhaled by roadside crowds. The impact of vehicle emission diffusion is comprehensively discussed in terms of vehicle emissions and roadside crowd exposure risk. A generalised impedance function considering the influence of vehicle exhaust emission diffusion is also established based on the weighted average of actual vehicle travel time, multi-model emissions and roadside crowd exposure risk. Finally, this generalised impedance function is integrated into the dynamic optimal user allocation model, and a dynamic traffic allocation model considering the influence of vehicle emission control is developed.

In the post-epidemic era, dynamic monitoring of expressway road freight volume is an important task. To accurately predict the daily freight volume of urban expressway, meteorological and other information are considered. Four commonly used algorithms, a random forest (RF), extreme gradient boosting (XGBoost), long short-term memory (LSTM) and K-nearest neighbour (KNN), are employed to predict freight volume based on expressway toll data sets, and a ridge regression method is used to fuse each algorithm. Nanjing and Suzhou in China are taken as a case study, using the meteorological data and freight volume data of the past week to predict the freight volume of the next day, next two days and three days. The performance of each algorithm is compared in terms of prediction accuracy and training time. The results show that in the forecast of freight volume in Nanjing, the overall prediction accuracies of the RF and XGBoost models are better; in the forecast of freight volume in Suzhou, the LSTM model has higher accuracy. The fusion forecasting method combines the advantages of each forecasting algorithm and presents the best results of forecasting the freight volumes in two cities.

Commuting contributes to high levels of greenhouse gases and air pollution. The recently advocated ‘green commuting’, i.e. active and public modes of transport, will be conducive to low-carbon and environmentally friendly transport. A baseline goal of urban planning is to promote health; however, few studies have explored the health-related impacts of environments at both ends of the commute on residents’ commuting mode choices. To fill the gap, this study proposes to consider the impact of the neighbourhood and working environment on green commuting from a health perspective. Using a sample of 15,886 people from 368 communities in China, three generalised multilevel linear regression models were estimated. Physical and psychological health were combined to further analyse health-related environmental attributes on the commuting choices of residents with different health levels. The results indicate that the working environment exerts more substantial effects on ‘green commuting’ than the neighbourhood environment, especially for workplace satisfaction. Moreover, we found that a good working environment and relationships will significantly encourage the sub-healthy group to choose active commuting. These findings are beneficial for policymakers to consider focusing on reconciling neighbourhood and working environments and meeting the commuting requirements of the less healthy group.

The application of electric vehicles (EVs) in the logistics industry has become more extensive. However, the mileage limitation of electric logistics vehicles (ELVs) and the long-distance distribution of ELVs have become urgent problems. Therefore, this paper proposes a long-distance distribution model for ELVs based on dynamic traffic information considering fleet mileage, distribution time and total distribution cost as the optimisation objectives, thus reasonably planning road selection and charging, and alleviating “mileage anxiety” in the long-distance distribution of ELVs. The model proposed in this paper comprehensively considers the characteristics of the high-speed and low-speed roads, the changes in road traffic flow on weekdays and non-weekdays, the time-of-use electricity price of electric vehicle charging stations (EVCSs) and uses the M/M/s queuing theory model to determine the charging waiting time. Finally, a real traffic network is taken as an example to verify the practicability and effectiveness of this model.

Urban intertunnel weaving (UIW) section is a special type of weaving section, where various lane-changing behaviours occur. To gain insight into the lane-changing behaviour in the UIW section, in this paper we attempt to analyse the decision feature and model the behaviour from the lane-changing point selection perspective. Based on field-collected lane-changing trajectory data, the lane-changing behaviours are divided into four types. Random forest method is applied to analyse the influencing factors of choice of lane-changing point. Moreover, a support vector machine model is adopted to perform decision behaviour modelling. Results reveal that there are significant differences in the influencing factors for different lane-changing types and different positions in the UIW segment. The three most important factor types are object vehicle status, current-lane rear vehicle status and target-lane rear vehicle status. The precision of the choice of lane-changing point models is at least 82%. The proposed method could reveal the detailed features of the lane-changing point selection behaviour in the UIW section and also provide a feasible choice of lane-changing point model.

Safety of rail vehicles is an important feature of sustainable public transport. Proofs of an effort in that area are new recommendations and regulations from the expert commission (WG2 of the Technical Committee CEN / TC 256) regarding trams and light rail vehicles aimed at vulnerable road users. Additional requirements on tram safety can be requested by the vehicle operator and/or city. Pedestrian safety measures can be adopted from the automotive sector utilising the protection principles from Regulation EC No. 78/2009, ECE/UN regulations, and EuroNCAP tests. The purpose of this publication is to introduce a simplified testing method for the tram front end with respect to pedestrian head-on collisions. Testing methods based on segment impactors were generally accepted. The wrap-around distance defines the assessment of vehicle impact areas. A mathematical model was created to compare the results of the full-scale tests and the segment tests done by the standard and simplified aluminium head impactors. The tram front-end design can be tested using this alternate method, based on a simple impactor and easy methodology, providing an efficient tool to inspire both the tram manufacturers and vehicle operators to improve the vulnerable road users’ safety in city traffic.

Based on the GPS trajectory data of a freight enterprise in Dalian, China, this paper studies the identification of loading and unloading points by a clustering algorithm. Firstly, by analysing the characteristics of freight loading and unloading behaviour, combined with the spatial and temporal distribution characteristics of truck GPS trajectory data, three characteristic variables of the number of trucks passing through a certain place, the average speed of trucks and the average stay time of trucks in the place are extracted. Then, the clustering algorithm and visual analysis are used to obtain the target cluster, and the POI language of the geographic information is obtained according to the points in the target cluster. The meaning information is crawled to accurately identify the result of the freight loading point. Finally, two classical clustering algorithms, K-means and GMM, are evaluated and compared. The results show that the identification method designed in this paper finally identifies 2,320 freight loading and unloading points from 11,406,000 trajectory data, which can realise the accurate extraction of freight loading and unloading points.

Transport is an integral part of any company. Nowadays, there is a great emphasis on the use of environmentally friendly modes of transport. In addition to being one of the environmentally friendly modes of transport, rail transport can carry large quantities of various goods over long distances. In order for rail transport in Slovakia to be able to compete with other modes of transport, it is important that Industry 4.0 elements are applied in the technological processes at railway stations. The aim of this article is to draw attention to the impact of the introduction of Industry 4.0 elements into the transport process in rail transport. The premise of the research task is based on the experience with the introduction of intelligent sensors in rail transport in some European Union countries. On the basis of the analysis of the use of information and communication technologies in railway transport, the article carries out a technological evaluation of the design of the wagon control unit in the transport process with regard to the speed of processing a shipment in a border-crossing station.

Numerous studies have shown that city bus drivers suffer from three key categories of health disorders: cardiovascular diseases, gastrointestinal disorders and musculoskeletal system issues, affecting the individual’s ability to work. The aim of this research was to assess the working ability of bus drivers and to determine the connection between the level of physical activity and the work ability in professional bus drivers. The study protocol included an assessment of participants’ work ability using the Work Ability Index (WAI) Questionnaire on a sample of 115 bus drivers. A statistical analysis was performed using the SAS System software package (SAS Institute Inc., North Carolina, USA). The questionnaire for determining the work ability index indicated good or excellent work ability in 78 (67.8%) of bus drivers. Moderate work ability that needed to be improved was recorded in 27 (23.5%) of drivers, and poor work ability that needed to be restored in 10 (8.7%). The results of the regression analysis show that increasing the average number of steps per day by a 1,000 increases the WAI score by 0.8. The obtained data should serve as an important argument for the design of future public health and kinesiology interventions to improve the work ability in professional bus drivers.