The aim of this work is to investigate the simplifica-tion of public transport networks (PTNs) for megacities and the optimisation of route planning based on the de-mand density of complex networks. A node deletion rule for network centre areas and a node merging rule for net-work border areas in the PTN are designed using the de-mand density of complex networks. A transit route plan-ning (TRP) model is established, which considers the demands of direct passengers, transfer passengers at the same stop and transfer passengers at different stops, and aims at maximising the transit demand density of a PTN. An optimisation process for TRP is developed based on the ant colony optimisation (ACO). The proposed method was validated through a sample application in Handan City in China. The results indicate that urban PTNs can be simplified while retaining their local attributes to a great extent. The hierarchical structure of the network is more obvious, and the layer-by-layer planning of routes can be effectively used in TRP. Moreover, the operating efficiency and service level of urban PTNs can be en-hanced effectively.

The vehicular Adhoc Network has unique charac-teristics of frequent topology changes, traffic rule-based node movement, and speculative travel pattern. It leads to stochastic unstable nature in forming clusters. The re-liable routing process and load balancing are essential to improve the network lifetime. Cluster formation is used to split the network topology into small structures. The reduced size network leads to accumulating the topology information quickly. Due to the absence of centralised management, there is a pitfall in network topology man-agement and optimal resource allocation, resulting in ineffective routing. Hence, it is necessary to develop an effective clustering algorithm for VANET. In this paper, the Genetic Algorithm (GA) and Dynamic Programming (DP) are used in designing load-balanced clusters. The proposed Angular Zone Augmented Elitism-Based Im-migrants GA (AZEIGA) used elitism-based immigrants GA to deal with the population and DP to store the out-come of old environments. AZEIGA ensures clustering of load-balanced nodes, which prolongs the network lifetime. Experimental results show that AZEIGA works appreciably well in homogeneous resource class VANET. The simulation proves that AZEIGA gave better perfor-mance in packet delivery, network lifetime, average de-lay, routing, and clustering overhead.

The efficiency of urban transportation system is un-der the influence of weather conditions. It is necessary to incorporate these impacts into transport system analysis, in order to prepare adequate mitigation measures. Trans-port models are often used in different types of transport system analysis and forecasting of its future characteris-tics. This paper focuses on implementation of the impact of rain in transport modelling, particularly into a traffic assignment process as a part of a macroscopic transport model. This aspect of modelling is important because it can indicate parts of the network where this impact leads to a high volume/capacity ratio, which is a good input for defining mitigation measures. Commonly, transport models do not consider weather impacts in its standard procedures. The paper presents a methodology for cali-brating volume-delay function in order to improve traf-fic assignment modelling in case of rain. The impact of different rain categories on capacity and free-flow speed was quantified and implemented in the volume-delay function. Special attention is given to the calibration of the part of volume-delay function for over-saturated traf-fic conditions. Calibration methodology is applicable for different types of volume-delay functions and presents a solid approach to incorporate weather conditions into common engineering practice.

The main competitor of air transportation is High-Speed Railway (HSR). However, in an oil-exporting country with low fuel prices and strong car dependence, HSR can face fierce competition with private cars and even buses. There is little previous research that forecast modal share in this situation. The case study of this research is the Tehran-Hamedan route in Iran that has high travel demand due to several historical and economic reasons and in the absence of air transportation, building the HSR in this route attracted foreign investment. To analyse the travel behaviour of passengers after the introduction of HSR, 409 stated and revealed preferences were collected in a self-designed questionnaire. Multinomial logit (MNL) model and mixed logit (ML) model were developed and modal share of each mode of transportation were forecasted up to 2045. HSR modal share is compared with other routes of the world to see the impact of air competition. The overall modal share of railway in this route is estimated to reach 64%, which is close to the average of major HSR routes globally (around 60%). Therefore, private cars can be a fierce competitor for HSR when there is no air link on the route and fuel is rather cheap.

In this study, a mobile application-based service providing information on the reduction in the air pollution source emissions due to the replacement of conventional scooters by electric scooters (e-scooters) was proposed to increase user awareness of air quality and purchase intention towards e-scooters. The extended unified theory of acceptance and use of technology was employed and an explanatory variable of environmental awareness (EA) was incorporated for enhancing constructs to investigate the factors that may influence the user acceptance of text-based mobile information on the reduced carbon emissions, in comparison with that of histogram-based mobile information on the reduction in emissions of six air pollution sources. A within-subjects experimental design was employed to evaluate both information contents. The results indicate that the model constructs of habit and EA are useful predictors of the behavioural intention (BI) to use app services. Furthermore, providing different mobile information contents demonstrated no statistically significant difference in the user’s acceptance and intentions. However, providing different mobile information contents on the same information spindle may trigger different constructs and intensities of influence on users’ purchase intentions for e-scooters. Based on these findings, several recommendations for app managers and developers and suggestions for future research have been provided.

Passenger stations are transit hubs where several railway lines interchange. They have important roles in providing train operations and passenger services. Interrelations between track layouts and technological performances are important for reducing bottleneck effects and raising the operational effectiveness of rail networks. To the best of our knowledge, in previous research the assessment of track layouts has not been considered with respect to various technological aspects including railway operations, safety, and passenger services but rather as a single criterion for analysis of different individual performance indicators. We propose a new two-phase decision making approach for the complex evaluation of track layout alternatives. The first phase model is a VIKOR method for ranking track layouts by criteria related to: railway capacity, safety issue, and passenger-pedestrian fluctuations. Next, in the second phase, we use marginal analysis to find Pareto front and compare the alternatives ratings by calculating performance-benefit coefficients. To show the applicability of the proposed model, we employ an illustrative example of a passenger rail station and evaluate six different track layout alternatives. The effectiveness of the proposed model is demonstrated comparing the proposed two-phase model with traditional VIKOR.

In this research, field and laboratory testing of three commercially available brake pads with the lowest, mid-dle, and highest price were performed. Complex field testing, where brake pads were tested in real extreme conditions on a loaded van vehicle and laboratory tests were performed. The field testing intended to investigate the temperatures that occur during the braking process and to determine the stopping distance, deceleration, and stopping time separately on the type of brake pads. Labo-ratory tests included the determination of the friction co-efficient according to ASTM G77, the structure of brake pad surfaces before and after the testing, and quantitative chemical analysis of brake pads. The aim of this study was to determine the influence of brake pad temperature on braking time depending on their purchase prices. The obtained results show a significant difference between the temperature, friction coefficient, chemical composi-tion, and braking time of the brake pads and their price.

At un-signalised at-grade intersections or roundabouts, motorcyclists have to make a quick decision to manoeuvre and avoid crash. Many studies show that risk-taking behaviour is the major cause of accidents in young motorcyclists. In this study, we analysed various factors that are involved in the risk-taking behaviour of young motorcyclists at un-signalised intersections. Online questionnaires were distributed among university and college students in Islamabad and Rawalpindi. The data of 490 respondents were collected to test the research model. Partial least square structural equation modelling approach was used to evaluate the measurement model, structural model and importance-performance map analysis. In this study, we assumed that risk-taking behaviour of young motorcyclists at un-signalised intersections could be influenced by several factors, i.e., demographic, past crash involvement, and peer influence. The results revealed that past crash involvement, confidence level, and peer influence were the significant factors that affect the risk-taking behaviour. Peer influence has the highest effect on the risk-taking behaviour. The person whose friends encourage them to take risk and accept challenges is more likely to exhibit the risk-taking behaviour. Those people who are more confident while riding a motorcycle are more likely to take risks.

Traffic congestion problems have dramatically esca-lated with the increasing volume of vehicles, pedestrians, and cyclists in the face of limited road capacity. This re-search aims to reduce the time road users spend in the system (school-zone area) and improve the efficiency of the process of dropping off and collecting children from a crowded school area. The study integrates discrete-event simulation (DES) and multi-criterion decision-mak-ing (MCDM) techniques to comprehensively evaluate the proposed alternatives to select an optimal solution based on many performance measures. A real-world case study of the traffic and congestion problems experienced by parents when they drop off and fetch their children from school during peak hours is presented. A heuristic algorithm was developed to simulate the random and un-predictable behaviour of road users. A cost-benefit anal-ysis considered the impact of waiting time, traffic den-sity, number of accidents, additional fuel expenses, and emission reduction. The technique for order of preference by similarity to ideal solution (TOPSIS) and preference selection index (PSI) methods were utilised to select the most appropriate option for parents. The study found that the integration of simulation techniques with MCDM methods could efficiently solve traffic problems.

The complexity of urban congestion requires policy-makers to adopt different congestion control measures that suit the characteristics of the city at the proper time. The paper focuses on the most controversial congestion pricing and offers methods to judge the efficacy of the policy by game theoretic approaches. It is found that congestion pricing is not merely a Pigouvian tax that internalizes drivers’ externalities, but also a powerful means to enhance public traffic proportion and balance road utilization on the premise of maximized social util-ity. Meanwhile, the embedded multiple case study shows that theoretical correctness of the policy is a necessary, but not sufficient condition for its effectiveness because the valid operation of the policy further requires cities to hold certain attributes in some aspects, such as econom-ic level, population density, proper pricing mechanism, and the ability to limit access to and from certain areas. Moreover, the authority should pay attention to matching the policy goal and its functions for successful implementation.

The advancement of data collection technologies has brought an upsurge in GPS applications. For example, travel behaviour research has benefited from the integration of multiple sources of Global Positioning System (GPS) data. However, the effective use of such data is still impeded by the challenge in data processing. For instance, GPS data, despite providing detailed spatial movement information, do not label the starting and finishing points of a trip, especially for commercial trucks. Hence, there is a critical need to develop a trip identification method to effectively use the trajectory data provided by GPS without additional information. This paper focused on identifying trips from the raw GPS data. Specifically, a systematic method is proposed to extract trips on the basis of origin-destination (OD) pairs by using a 5-step procedure. An application was provided on estimating the performance of travel time reliability using three metrics based on the OD trips for each dedicated truck. The application showed that, in general, trucks on long-distance routes have less reliable travel times compared to trucks on short-distance routes. This paper provides an example of using GPS data, without further information, to study travel time for freight performance and similar needs of punctuality in logistics.

The outbreak of COVID-19 disrupted our everyday life. Many local authorities enforced a cordon sanitaire for the protection of sensitive areas. Travellers can only pass the cordon after tested. This paper aims to propose a method to design an on-ramp control scheme to maximise urban freeway network throughput with a predetermined queuing delay constraint at all off-ramps around cordon sanitaire. A bi-level programming model is formulated where the lower-level is a transportation system equilibrium to predict traffic flow, and the upper-level is onramp metering optimisation that is nonlinear programming. A stochastic queuing model is used to represent the waiting phenomenon at each off-ramp where testing is conducted, and a heuristic algorithm is designed to solve the proposed bi-level model where a method of successive averages (MSA) is adopted for the lower-level model; A genetic algorithm (GA) with elite strategy is adopted for the upper-level model. An experimental study is conducted to demonstrate the effectiveness of the proposed method and algorithm. The results show that the methods can find a good heuristic optimal solution. These methods are useful for freeway operators to determine the optimal on-ramp control for disease control and prevention.