In recent years, the public’s interaction with street green spaces has been increasing, leading to much more concern about its design. By using stated preference data from a discrete choice experiment and the multinomial logit model, this study investigates pedestrians’ and cy-clists’ landscape preference regarding street green space through an online survey based on a virtual street envi-ronment. The results show that trees are the most suitable to be planted symmetrically between the cycle track and sidewalk. Large size trees with large crown width and tall height are more preferred than common size trees. There are considerable differences in preferences for lo-cations of shrubs, hedges, flowers, and grass between cy-clists and pedestrians. Cyclists prefer grass by the cycle track the most and grass by the sidewalk the least. But for pedestrians, flowers, hedges, and grass by the sidewalk are positively significant. Buildings with green plants in their front yards are preferred over a monotonous facade or coffee seats. This study enriches the understanding of the public’s landscape preferences for streets sharing non-motorised lanes. The results also play a guiding role in people-oriented street green space designs of land-scape architects and governments.

Traffic congestion has become a severe problem, af-fecting travellers both mentally and economically. To al-leviate traffic congestion, this paper proposes a method using a concept of future time windows to estimate the future state of the road network for navigation. Through our method, we can estimate the travel time not only based on the current traffic state, but the state that ve-hicles will arrive in the future. To test our method, we conduct experiments based on Simulation of Urban MO-bility (SUMO). The experimental results show that the proposed method can significantly reduce the overall travel time of all vehicles, compared to the benchmark Dijkstra algorithm. We also compared our method to the Dynamic User Equilibrium (DUE) provided by SUMO. The experimental results show that the performance of our method is a little better than the DUE. In practice, the proposed method takes less time for computation and is insensitive to low driver compliance: with as low as 40% compliance rate, our method can significantly im-prove the efficiency of the unsignalised road network. We also verify the effectiveness of our method in a signalised road network. It also demonstrates that our method can assign traffic efficiently.

Roundabout capacity estimation has been the subject of several types of research in recent decades. Most of the analyses are based on the empirical or analytical models (e.g. gap acceptance theory) considering various kinds of conflicting flows, namely entry, circulating, and exit-ing flow. The drivers on the exiting flow either obey the traffic rule (use the right-turn indicator) or disobey the traffic rule (do not use the right-turn indicator). Accord-ing to the reviewed literature, the impact of these driv-ers on the roundabout capacity has not been studied to a greater extent. Therefore, this study aims to develop an analytical roundabout capacity estimation model that also takes into account a share of exiting flow. It extends Brilon-Wu’s model, by including the impact of exiting drivers who disobey the traffic rule on the gap accep-tance of the entering drivers. The proposed model was validated using the quasi-observation data generated by a microscopic model. The results obtained by our model were compared with Bovy’ and Yaps’ empirical models as well as Brilon-Wu’s analytical model for a single-lane roundabout. Using the RMSE and regression analysis, it is proved that the proposed model outperforms the exist-ing models in terms of estimating the capacity and delays of roundabouts.

Short-term traffic flow prediction is to automatically predict the traffic flow changes in a period of future time based on the extraction of the spatiotemporal features in the road network. For governments, timely and accurate traffic flow prediction is crucial to plan road manage-ment and improve traffic efficiency. Recent advances in deep learning have shown their dominance on short-term traffic flow prediction. However, previous methods based on deep learning are mainly limited to temporal features and have so far failed to predict the bidirectional con-textual spatiotemporal relationship correctly. Besides, the precision and the practicality are limited by the road network scale and the single time scale. To remedy these issues, a Bidirectional Context-aware and Multi-scale fusion hybrid Network (BCM-Net) is proposed, which is a novel short-term traffic flow prediction framework to predict timely and accurate traffic flow changes. In BCM-Net, the Bidirectional Context-aware (BCM) block is added to the feature extraction structure to effective-ly integrate spatiotemporal features. The Interpolation Back Propagation sub-network is used to merge multi-scale information, which further improves the robustness of the model. Experiment results on diverse datasets demonstrated that the proposed method outperformed the state-of-the-art methods.

Work-related road accidents are estimated to con-tribute to at least one quarter to over one third of all work-related deaths. Changing the vehicle has a major impact on traffic safety. Some studies have shown that drivers’ knowledge and practical driver training can improve traffic safety when changing vehicles. The aim of this paper is to determine whether there is an impact of the vehicle change on traffic safety. The research was conducted at the location with cylinders, braking coeffi-cient sensors, and brake pedal force detector, as well as with ten different passenger car brands and types. At the time of the research, all cars were registered and used daily in traffic. Prior to the research, the precision of the measuring instruments at the research site was checked. On the basis of the results, it can be concluded that there are two significant factors: the vehicle and the driver who needs to be trained before starting to drive a new ve-hicle. When changing the vehicle brand and type within the company, it is necessary to conduct systemic training of drivers which would include theoretical and practical parts and involve at least braking, driver distraction, and active and passive vehicle safety.

In the current world of increasing density of unmanned aerial vehicle operations in the airspace, there is an enhanced emphasis on their safety due to the potential for mid-air collision, either with another aircraft or with each other. At the same time, unmanned aerial vehicles are also being used in the context of introducing smart technologies into maintenance processes, where there is also a need to prevent a potentially possible conflict when two drones come close together. The paper introduces a mathematical model for tactical prediction of a conflict between a pair of drones. The tactical prediction of drone conflict is intended to alert the drone operator to an immediate potentially dangerous situation. The mathematical simulation in this paper extrapolates the 3D trajectory in the direction of the relative velocity vector of the convergence over the advance time. If the extrapolated trajectory has at least one point in common with the conflict space of the other drone, the conflict is signalled to the drone operator. This model can then be used in practice to simulate flight operations in shared airspace or to develop the currently required rules in selected situations.

Driver distraction has been identified as a contributing factor to road crashes, among which the most common is the use of mobile phones while driving. For this reason, the aim of this paper is to analyse the behaviour of young drivers while they use mobile phones (answering a telephone call, texting, and browsing the internet) and drive in a simulated urban environment. In total 28 volunteers participated in the study. Several variables were recorded for each participant: driving speed, acceleration, deceleration, and eye movement. The results show that the difference in driving speed, acceleration, and deceleration was relatively small for each task and for the control condition (no use of mobile phone). However, when looking at the total time required for conducting each task, participants spent 26.44% of the time looking at the phone when texting, 37.01% when browsing the internet, and 2.27% when talking on the phone. In addition, participants viewed on average 66.45% traffic signs when distracted, compared to 79.22% during undistracted driving. Based on the results, a proactive approach to reduce the problem related to the use of mobile phones while driving is proposed.

Interlockings are an essential element of the railway system. They are necessary to command and control devices, such as points and signals in order to route trains within the bounds of railway stations. Their design must ensure the highest level of safety for all involved parties. The European continent has an extensive railway network which has slowly grown over more than 150 years. Interlockings have evolved over the same period from large mechanical devices requiring physical force to operate to computerised systems capable of complex operations. Despite the technological leap, many interlockings using older technologies are still in use in the present. This review aims to paint an accurate picture of the current state of interlockings in Europe by evaluating the share of each interlocking generation (mechanical, relay and electronic). The study covers 15 countries and over 200,000 km of railway tracks, representing over two thirds of the entire EU railway network. A brief presentation is given for each country, while comparisons made between the researched countries highlight certain key findings. The focus is only on station interlockings, not including line signalling. The conclusions of this analysis include recommendations for current and future development of the railway sector.

Passenger choice behaviour of buying tickets has a great impact on the high-speed rail (HSR) revenue management. It is very critical to find out the sensitive factors that prevent passengers with high willingness to pay for a ticket from buying low-price tickets. The literature on passenger choice behaviour mainly focuses on travel mode choice, choice between a conventional train and a high-speed train and choice among high-speed trains. To extend the literature and serve revenue management, this paper investigates passenger choice behaviour of buying high-speed railway tickets. The data were collected by the stated preference (SP) survey based on Beijing-Hohhot high-speed railway. The conditional logit model was established to analyse influencing factors for business travel and non-business travel. The results show that: business passengers have the higher inherent preference for full-price tickets, while non-business passengers have the higher inherent preference for discount tickets; the number of days booked in advance and frequent passenger points have a significant impact on the ticket choice of business travellers, but not on non-business travellers; passengers are unwilling to buy tickets that depart after 16:00 for non-business travel; factors have different effects on the passengers' choice in business travel and non-business travel. The results can provide parameters for revenue management models and references for the ticket-product design.

This paper presents the modelling of the saturation flow rate of the permitted left turn in an exclusive lane. In the proposed model, the total permitted left-turn saturation flow rate is determined as a sum of saturation flow rates during the effective green time and the intergreen period. Primarily, the permitted left-turn saturation flow rate during the effective green time is modelled based on the opposing through-flow degree of saturation and the number of opposing through-flow lanes. The relation between the permitted left-turn saturation flow during the effective green time and these variables was examined using data from the simulation experiments in VISSIM. To our knowledge, this is the first study of the permitted left-turn saturation flow modelling based on the opposing through-flow degree of saturation instead of the opposing through-flow rate and signal-timing parameters. The proposed model was validated based on data collected at seven intersections with a permitted left turn served in an exclusive lane. The permitted left-turn saturation flow rate could be accurately determined based on the opposing through-flow degree of saturation and the number of opposing lanes according to the RMSE of 58.4 pcu/h.

Customised bus (CB) is a cutting-edge mean of transportation and has been implemented worldwide. To support the spread of the CB system, methodologies for CB network design have been conducted. However, a majority of them cannot be adopted directly for multi-modal transportation environment. In this paper, we proposed a bi-level programming model to fill this gap. The upper-level problem is to maximise the usage of the CB system with the limitation of operation constraints. Meanwhile, the lower-level problem is to capture the traveller’s choice by minimising traveller’s generalised cost during travel. A solving procedure via genetic algorithm is further proposed and validated via the metro data at Shanghai. The results indicated that the proposed CB route network would attract nearly 5,000 users during morning peak period under the given metro transaction data. We further studied the features of the selected routes and found that the CB network mainly served residence to commercial or industrial parks travellers and would provide travel service with fewer stops, and higher travel efficiency by travelling through expressway.

Support vector machine (SVM) models have good performance in predicting daily traffic volume at toll stations, however, they cannot accurately predict holiday traffic volume. Therefore, an improved SVM model is proposed in this paper. The paper takes a toll station in Heilongjiang, China as an example, and uses the daily traffic volume as the learning set. The current and previous 7-day traffic volumes are used as the dependent and independent variables for model learning, respectively. This paper found that the basic SVM model is not  accurate enough to forecast the traffic volume during holidays. To improve the model accuracy, this paper first used the SVM model to forecast non-holiday traffic volumes, and proposed a prediction method using quarterly conversion coefficients combined with the SVM model to construct an improved SVM model. The result of the prediction showed that the improved SVM model in this paper was able to effectively improve accuracy, making it better than in the basic SVM and GBDT model, thus proving the feasibility of the improved SVM model.