Cost-benefit analysis (CBA) is the universally applied tool to assess economic viability in assisting decisions on transport investments. Its framework is heavily influenced by the numerous variables it considers through estimating and valuing the intervention’s effects. This paper – utilising the authors’ previously implemented CBA test environment – comprehensively analyses the sensitivity of significant variables of three typical CBA models for transport interventions (road, rail and urban) to understand the prevailing appraisal approach better and to help focus on further methodological improvements. Morris and Sobol methods were selected to study the global sensitivity of and the relations between the input parameters of the models. The sensitivity test of the three analysed models provided similar results regarding which variables are most influential in CBAs. Input variables such as the investment cost, the economic discount rate, forecasted GDP changes and specific elasticities to these GDP changes often have a firm but mostly linear effect. Value of time, vehicle operating cost and mode choice-related parameters such as car availability, car occupancy rate, level of service indicators (e.g. frequency of service) and potential to induce travel demand (proxied by a ‘no travel’ parameter) are inputs with considerable linear effects and greater interactive effects.

The transportation sector wields substantial influence on society, encompassing economic, social and environmental dimensions of sustainability. Recognising environmentally conscious actions initiated by individuals, particularly at grassroots levels, fosters the development of a pro-environmental social identity. The article aims to analyse the transportation systems from a bottom-up perspective within a municipality. Consequently, three objectives are proposed for this research paper: investigate citizen behaviour regarding transportation, assess the strengths and weaknesses of communities based on citizen perspectives and generate ideas for improving transit through responsible management principles using a bottom-up approach. It has been determined that private car is the most commonly used mode of transportation. The number of cars is the only variable that influences the choice of transportation. A significant positive relationship has been identified between the number of cars and car travels, while a negative relationship has been observed between the number of cars and travels by transit, pedestrian or bicycle. In addition to this, other significant relationships were determined. Regarding the second objective, the majority of the interviewees perceive that the commune lacks any significant strengths. In terms of enhancement opportunities, respondents express a desire for improvements in pedestrian and cyclist infrastructure, transit facilities and the addition of more lanes and roads.

In this paper, we consider the problem of minimising the cost of data transmission as a function of the capacity of telecommunication links. To solve this problem, we first formulated a mathematical model, and then we designed and developed a software that enables the optimisation of the given or randomly generated telecommunications network. Declarative programming is a good choice for optimisation problems because it is enough to specify only the relations that must be satisfied, without giving any effective procedure for finding the values for the decision variables. To test the application, we developed a software that randomly generates a telecommunications network that meets the given requirements. This enables us to test the application on an arbitrary number of different telecommunication networks with different numbers of nodes and links, and analyse the impact of changing network parameters on the flow and results of the optimisation. As telecommunications networks operate in conditions of uncertainty, the subject of special analysis was the potential failure of some of the network links. The paper presents and thoroughly analyses the optimisation results for several selected networks, as well as summary results for a number of telecommunications networks.

Accurately predicting taxi-in times for arrival flights is crucial for efficient ground handling resource allocation, impacting flight departure timeliness. This study investigates terminal layout characteristics, specifically decentralised layouts, to predict and analyse arrival flight taxi-in times. We develop a surface traffic flow calculation method considering arrival and departure flights, eliminating fixed thresholds. We introduce runway-crossing operations for decentralised airports, creating new prediction variables. We consider factors like runway, aircraft type, airline, taxi distance, and time periods. Gradient Boosting Regression Tree predicts taxi-in times, while Lasso analyses factor impact. Our approach yields highly accurate predictions for decentralised airports, with Surface traffic flow and Runway-crossing variables significantly influencing taxi-in times. This research informs airport managers in decentralised layouts, enabling tailored management strategies.

This study aims to quantitatively assess the adjustment effects of various visual guiding schemes on the abrupt change of vehicle trajectory. A driving simulation experiment was conducted using five simulated scenes: (1) baseline (actual situation), (2) pavement (road studs), (3) low position (flexible guideposts), (4) high position (warning alignment signs and retroreflective arches) and (5) multilayer (combination of all devices). Raw data, including vehicle positions, steering wheel angles and lateral offset, were collected. Based on these data, the gradual change degree of vehicle trajectory (G) and average steering wheel angle (SWAav) were computed to quantitatively evaluate the extent of vehicle trajectory deviation and the stability of steering wheel operations respectively. These two evaluation indicators were then translated into trajectory gradualness (TG) and operation stability (OS), respectively, to assess the adjustment effects of different visual guiding schemes. The study results demonstrate that road studs perform a certain degree of enhancement on operation stability (OS). Flexible guideposts exhibit the best effects on operation stability (OS). Additionally, the combination of warning alignment signs and retroreflective arches demonstrate the best regulation of trajectory gradualness (TG). Multilayer visual guiding system achieves the optimal trajectory gradualness (TG) and operation stability (OS).

The emergence of battery electric buses (BEBs) can alleviate environmental problems caused by tailpipe emissions in transit system. However, the high cost of on-board batteries and range anxiety hinder its further development. Recently, the advent of dynamic wireless power transfer technology (DWPT) has become a potential solution to promote the development of BEBs. Hence, this study focuses on the application of DWPT in flex-route transit system. A mixed integer non-linear model is proposed to simultaneously optimise the bus routing and the selection of corresponding bus types considering the constraints of passengers’ travel time, battery size and bus capacity. The objective is to minimise both transit agency cost and passengers’ travel time cost. A tangible hybrid variable neighbourhood search (HVNS) consisting of simulated annealing (SA) and variable neighbourhood search (VNS) is developed to solve the proposed model efficiently. Compared with GAMS (DICOPT solver) and VNS, the proposed algorithm can considerably improve computational efficiency. The results suggest that the proposed model can effectively determine the BEBs’ routing and bus type for flex-route transit system powered by DWPT through a case study in Xi’an China. A comparative analysis shows the proposed model takes 12.97% less total cost than the alternative model with terminal charging technology (TCT).

Weaving sections on roads are crucial areas with high concentrations of mandatory lane changes, which can increase the likelihood of traffic accidents. Speed is a key factor in determining traffic safety, and the development of an accurate speed prediction method is essential for improving safety in weaving sections. While current methods are effective in predicting speeds in straightforward situations, they face challenges in more complex scenarios such as weaving sections. This study presents a refined traffic speed prediction approach specifically designed for weaving sections in order to tackle the aforementioned issue. Initially, novel variables were formulated to capture the unique traffic flow attributes present in weaving sections, which distinguish them from standard road segments. Subsequently, supplementary empirical variables that are known to impact speed were incorporated. We conducted a variable importance assessment to ascertain the extent and direction of each variable’s contribution. Lastly, variables with significant positive effects were chosen as inputs for three machine learning algorithms: Random Forest (RF), Backpropagation Neural Network optimised with Genetic Algorithm (BPNN-GA) and Support Vector Regression (SVR). This method was evaluated using aerial footage from five distinct weaving sections in China, maintaining an approximately 3 km/h prediction error. In addition, the study also finds that the speed distribution in weaving sections is negatively correlated with the number of lane-changing. Vehicles experience deceleration at both on-ramp and off-ramp, with a more significant deceleration occurring at the on-ramp. Speed is significantly affected by short length, number of lanes and proportion of large vehicles. The proposed method can be embedded into intelligent traffic systems for safe speeds of autonomous vehicles in weaving sections. Reconstructing spatiotemporal patterns of traffic congestion, predicting traffic accidents and implementing active traffic management strategies in weaving sections could be investigated in the future.

The popularisation of autonomous vehicles will give rise to a new business model called shared autonomous vehicle (SAV). SAVs may attract a large number of passengers and lead to a decline in the share of buses, which can be interpreted by exploring passengers’ travel behaviour when confronting the SAV and bus modes. Thus, this paper addresses the SAV and bus passengers’ travel behaviour, aiming to examine the factors influencing travel behaviour and revealing the characteristics of SAV passengers. We classified passengers using latent class cluster analysis and modelled passengers’ travel behaviour based on confirmatory factor analysis and mixed logit model. The findings indicate a variation in travel preferences among different classes of travellers. Short-distance travellers pay less attention to travel time. Non-short-distance PT travellers are most likely to be affected by service attributes (waiting time, travel time and travel costs). Non-short-distance private car travellers are more likely to become early SAV adopters. Passengers travelling for short distances may be more likely to choose SAV, which reveals the potential of SAVs to become a first and last mile connection for public transport. Passengers lack trust in SAVs, which will affect their promotion.

The pedestrian cognitive load has an important effect on the pedestrian crossing decision making. Compared with young adults, old people are characterised by declining physical function and slower reaction ability, which makes them prone to traffic accidents when crossing the street. This study aims to compare the visual information-mental load correlation between elderly and young adults waiting at the signalised intersections and evaluate their cognitive load conditions. Therefore, two signalised intersections with different traffic scenes in the Nan’an District, Chongqing, China were selected. The eye-tracking, electrocardiographic and electrodermal activity data of young and old pedestrians were collected using eye-tracking and physiological instruments. The visual indexes (the total duration of fixations, the number of fixations, the average pupil diameter changing rate, the number of saccades, the average peak speed of saccades, the average amplitude of saccades and the total amplitude of saccades) and physiological indicators (the average growth rate of heart rate, the time-domain analysis indicator of HRV and HRV frequency domain analysis indicators, electrodermal response amplitude and rise time of the EDR amp.) were taken as inputs and outputs parameters, respectively. Then, the comprehensive cognitive load evaluation model for pedestrians was constructed when waiting to cross the street based on the data envelopment analysis method. And the cognitive load characteristic differences between the young adults and the elderly were compared. The results show that in the same crossing scene, compared with the young pedestrian, the elder pedestrian exhibited lower overall perceptual efficiency, lower fixation durations and higher cognitive loads. These results can provide certain references on improving the street crossing safety for the elderly pedestrian.

Expressway weaving areas meet dissipative structure characteristics. When traffic states reach a certain range, they exhibit self-organising criticality, and slight changes may trigger unpredictable congestion. This paper examines the correlation between the dissipative structure of the weaving area and key traffic parameters. The range of dissipative structure states in the weaving area is defined through the dissipative structure concept with three-phase traffic flow theory and real traffic data. Based on the fundamental diagram and measured traffic data, the weaving area dissipative structure model characterising the relationship between critical state changes in traffic volume is constructed and validated. Finally, the Cell Transmission Model simulation was used to examine the characteristic relationship between the weaving area dissipative structure state duration, the weaving area length and the weaving flow ratio. The results show that the length of the dissipative structure state is maintained when the traffic flow is self-organised into a free-flow or a congested state positively correlates with the length of the weaving area. Higher weaving flow ratios lead to shorter dissipative structure state durations during congestion formation, and the exact opposite during congestion evacuation. This paper is important for analysing the congestion mechanism and managing congestion.

In order to enhance the driving ability of autonomous vehicles on structured roads and enable them to plan safe and comfortable paths, we propose an obstacle avoidance path strategy for autonomous vehicles based on genetic algorithm. The use of Frenet-Serret enhances the adaptability of the algorithm in complex environments. In order to improve the generation and optimisation of obstacle avoidance trajectory, we establish an anti-collision model. When the vehicle faces a potential collision with an obstacle, the genetic algorithm quickly iterates and selects the first nine genes to generate the rough solution and convex space of the path. Combined with convex space, the quadratic programming method will numerically optimise the generated rough solution to generate an accurate path that satisfies the constraints. In addition, in order to ensure the safety and comfort in the process of obstacle avoidance, based on the dynamic constraints of the vehicle, the speed planning is used to determine the speed curve. We simulate in various scenarios involving moving obstacles. The real-time simulation based on the HIL platform proves that the proposed path planning strategy is effective in various driving scenarios.

Data collection technologies or data sources are critical for highway network management. However, due to the limitations on available management resources, determining the importance of these data sources is necessary to allocate these resources reasonably. This study proposes a complex network based method for evaluating the importance of multiple data sources in highway networks. This method includes mainly three steps. First, the business-data source relation will be identified and formulated for the highway network. Second, a business data source complex network is built from the previously identified business-data relationship. Third, an entropy weight method is used to compute and rank the importance of data source nodes by combining three indexes of degree centrality (DC), closeness centrality (CC) and structural holes (SC) computed based on the complex network. The proposed method is applied and illustrated using the highway network of Xuzhou City, Jiangsu Province, China. The results show that among the data sources, the most important data source is the continuous traffic survey station, followed by an automatic gantry-based station and vehicle detectors-based system. Discussions on the limitations, applications and future studies are provided for the proposed approach.

Predicting traffic speed accurately and in real-time is crucial for the development of smart transportation systems. Given the nonlinear and stochastic nature of vehicle data, integrating diverse spatio-temporal data sources with the Improved Particle Swarm Optimisation (IPSO) offers a promising approach to optimise the Long Short-Term Memory Neural Network (LSTM). Firstly, we enhance the optimisation capabilities of PSO by implementing nonlinear inertial weight and adaptive variation. Secondly, addressing the challenge of selecting the LSTM hyperparameters, the PSO algorithm effectively identifies global optimal solutions for hyperparameter optimisation, ensuring appropriate settings through iterative training. Subsequently, we conduct a case study using multi-source spatio-temporal traffic speed data, comparing our proposed IPSO-LSTM model with traditional neural network prediction models and advanced models. Results from the experiment demonstrate that the IPSO-LSTM model presented in this study addresses issues of parameter selection and inaccurate prediction encountered by traditional LSTM models in traffic state prediction. Moreover, it enhances the model’s ability to capture speed time series dynamics. Notably, in processing complex speed data, our model exhibits superior accuracy and stability in prediction.