This paper analyses the scientific study on airport efficiency in the WoS (Web of Science) database for the period 2000–2019. Productivity indicators have been obtained by author, years, journals, and institutions, and an analysis of visibility, impact, and scientific collaboration through co-citations was performed. The areas of greatest application are transport, engineering, and economics. This study reveals the existence of three research lines, one on airport safety and management, another on technical efficiency using mainly the DEA method, and the third associated with airport regularization issues. An important issue which is increasingly taken into account in efficiency studies is related to environmental aspects. In the ranking of journals publishing on airport efficiency, ordered by number of articles indexed in WoS, Journal of Air Transport Management is the one with the highest number of cited articles and publications, whereas Sustainability stands out as the first non-specific journal on transport. The University of Lisbon and the University of British Columbia are the ones that deal most with airport efficiency.

The airline industry has shown significant growth in the last decade according to some indicators such as annual average growth in global air traffic passenger demand and growth rate in the global air transport fleet. This inevitable progress makes the airline industry challenging and forces airline companies to produce a range of solutions that increase consumer loyalty to the brand. These solutions to reduce the high costs encountered in airline operations, prevent delays in planned departure times, improve service quality, or reduce environmental impacts can be diversified according to the need. Although one can refer to past surveys, it is not sufficient to cover the rich literature of airline scheduling, especially for the last decade. This study aims to fill this gap by reviewing the airline operations related papers published between 2009 and 2019, and focus on the ones especially in the aircraft maintenance routing area which seems a promising branch.

The concept of sharing transport infrastructure has become increasingly prominent in the sustainable society due to limited resources in urban cities. Shared lanes where cars, electric motorcycles, and bicycles are permitted have been promoted in urban China to overcome the shortage of road space available to meet the increasing traffic demand. Based on VR video and questionnaire survey, this study has identified that the levels of satisfaction of drivers, e-motorcyclists, and cyclists are associated with the factors of traffic condition and lane characteristics among various shared lanes. Based on the analysis of data by a multinomial ordered logistic regression model, the major findings of this study are summarised as follows: (1) Satisfaction was mainly affected by lane width, lane number, lane type, and presence of parking space in the driver group. (2) Lane width, lane number, lane type, presence of parking space, speed, and lateral separation were the main factors in the e-motorcyclist group. (3) For the group of cyclists, lane width, lane number, presence of slope, presence of parking space, speed, and lateral separation were identified as the main factors. Our study will help local government officials to design more effective sustainable transport infrastructures.

The paper tests for statistical association between employment and value added of freight transport industry and its component activities against overall economy in a ten-year panel ranging from 2008 to 2017 of the thirteen newest European Union member countries. In this paper, the nature of correlation between economic growth as the independent variable and freight transport industry as a dependent variable is examined. To achieve stationarity, and to lose autocorrelation and the idiosyncratic effects, the variables are first differenced. The results of the “Granger causality” tests show the null hypothesis of no-causation may be rejected for most conjectures with high F-Statistics as well as high statistical significance. The results of the Panel EGLS cross-section fixed effects do not reject the results gained by the Granger test, and the same may be said for the Panel Generalised Method of Moments First Differences test. The result of the Arellano-Bond test shows no serial correlation in the residuals. It has been concluded that changes in overall economy (value added and employment) have a significant and measurably strong impact on freight transportation and warehousing sector. This conclusion is useful in assessing future impacts on freight transport industry, especially as a consequence of contingent events.

The recharging plans are a key component of the electric bus schedule. Since the real-world charging function of electric vehicles follows a nonlinear relationship with the charging duration, it is challenging to accurately estimate the charging time. To provide a feasible bus schedule given the nonlinear charging function, this paper proposes a mixed integer programming model with a piecewise linear charging approximation and multi-depot and multi-vehicle type scheduling. The objective of the model is to minimise the total cost of the schedule, which includes the vehicle purchasing cost and operation cost. From a practical point of view, the number of line changes of each bus is also taken as one of the constraints in the optimisation. An improved heuristic algorithm is then proposed to find high-quality solutions of the problem with an efficient computation. Finally, a real-world dataset is used for the case study. The results of using different charging functions indicate a large deviation between the linear charging function and the piecewise linear approximation, which can effectively avoid the infeasible bus schedules. Moreover, the experiments show that the proposed line change constraints can be an effective control method for transit operators.

Real-time transit information (RTI) service can provide travellers with information on public transport and guide them to arrange departure time and travel mode accordingly. This paper aims to analyse travellers’ choices under RTI by exploring the relationship between the related variables of RTI and passengers’ travel choice. Based on the stated preference (SP) survey data, the ordinal logistic regression model is established to analyse the changing probability of passengers’ travel behaviour under RTI. The model calculation results show that travellers getting off work are more likely to change their travel choice under RTI. When data from the control and experimental groups are compared, the differences in route selection are significant. Specifically, passengers with RTI have a more complex route selection than those without, including their changes of travel mode, departure time, vehicles, and stop choices. The research findings can provide insights into the optimisation of intelligent transit information systems and the strategy of RTI. Also, the analysis of passengers’ travel choice under RTI in the transit network can help to improve network planning.

The need to make effective plans for locating transportation hubs is of increasing importance in the megaregional area, as recent research suggests that the growing intercity travel demand affects the efficiency of a megaregional transportation system. This paper investigates a hierarchical facility location problem in a megaregional passenger transportation network. The aim of the study is to determine the locations of hub facilities at different hierarchical levels and distribute the demands to these facilities with minimum total cost, including investment, transportation, and congestion costs. The problem is formulated as a mixed-integer nonlinear programming model considering the service availability structure and hub congestion effects. A case study is designed to demonstrate the effectiveness of the proposed model in the Wuhan metropolitan area. The results show that the congestion effects can be addressed by reallocating the demand to balance the hub utilisation or constructing new hubs to increase the network capacity. The methods of appropriately locating hubs and distributing traffic flows are proposed to optimise the megaregional passenger transportation networks, which has important implications for decision makers.

Individual differences (IDs) may reduce the detection-accuracy of drowsiness-driving by influencing measurements’ drowsiness-detection performance (MDDP). The purpose of this paper is to propose a model that can quantify the effects of IDs on MDDP and find measurements with less impact by IDs to build drowsiness-detection models. Through field experiments, drivers’ naturalistic driving data and subjective-drowsiness levels were collected, and drowsiness-related measurements were calculated using the double-layer sliding time window. In the model, MDDP was represented by |Z-statistics| of the Wilcoxon-test. First, the individual driver’s measurements were analysed by Wilcoxon-test. Next, drivers were combined in pairs, measurements of paired-driver combinations were analysed by Wilcoxon-test, and measurement’s IDs of paired-driver combinations were calculated. Finally, linear regression was used to fit the measurements’ IDs and changes of MDDP that equalled the individual driver’s |Z-statistics| minus the paired-driver combination’s |Z-statistics|, and the slope’s absolute value (|k|) indicated the effects of ID on the MDDP. As a result, |k| of the mean of the percentage of eyelid closure (MPECL) is the lowest (4.95), which illustrates MPECL is the least affected by IDs. The results contribute to the measurement selection of drowsiness-detection models considering IDs.

The ever-increasing travel demand outpacing available transportation capacity especially in the U.S. urban areas has led to more severe traffic congestion and delays. This study proposes a methodology for intersection signal timing optimisation for an urban street network aimed to minimise intersection-related delays by dynamically adjusting green splits of signal timing plans designed for intersections in an urban street network in each hour of the day in response to varying traffic entering the intersections. Two options are considered in optimisation formulation, which are concerned with minimising vehicle delays per cycle, and minimising weighted vehicle and pedestrian delays per cycle calculated using the 2010 Highway Capacity Manual (HCM) method. The hourly vehicular traffic is derived by progressively executing a regional travel demand forecasting model that could handle interactions between signal timing plans and predicted vehicular traffic entering intersections, coupled with pedestrian crossing counts. A computational study is conducted for methodology application to the central business district (CBD) street network in Chicago, USA. Relative weights for calculating weighted vehicle and pedestrian delays, and intersection degrees of saturation are revealed to be significant factors affecting the effectiveness of network-wide signal timing optimisation. For the current study, delay reductions are maximised using a weighting split of 78/22 between vehicle and pedestrian delays.

Accurate traffic prediction on a large-scale road network is significant for traffic operations and management. In this study, we propose an equation for achieving a comprehensive and accurate prediction that effectively combines traffic data and non-traffic data. Based on that, we developed a novel prediction model, called the adaptive deep neural network (ADNN). In the ADNN, we use two long short-term memory (LSTM) networks to extract spatial-temporal characteristics and temporal characteristics, respectively. A backpropagation neural network (BPNN) is also employed to represent situations from contextual factors such as station index, forecast horizon, and weather. The experimental results show that the prediction of ADNN for different stations and different forecast horizons has high accuracy; even for one hour ahead, its performance is also satisfactory. The comparison of ADNN and several benchmark prediction models also indicates the robustness of the ADNN.

Mopeds (electric bicycles and light motorcycles) are commonly used as a personal transportation mode in China. However, the understanding of characteristics of left-turning mopeds at signal-controlled intersections has been relatively limited. To bridge this gap, firstly, this paper proposed a video conversion method of moped movement data acquisition. Then, a method of data accuracy verification was introduced by comparing the results between the field experiment and the video conversion method. Secondly, the ideal traffic space for left-turn mopeds from different entrances was defined to analyse the characteristics of the left-turning mopeds at intersections. Further, three indicators, namely, transverse distance, the proportion of left-turning mopeds with crossing behaviour, and the average number of avoidance behaviour, were proposed and used to analyse the asymmetrical characteristics behaviour, crossing behaviour, and avoidance behaviour. Finally, based on empirical data collected from five signal-controlled intersections, the proposed methods and behaviours were analysed. This paper provides both a valid method of obtaining the position data of mopeds and a reliable basis for improving the safety of left-turning moped riders at urban signal-controlled intersections.

This paper presents the design of a fuzzy logic-based traffic scheduling algorithm aimed at reducing traffic congestion for the case of partial obstruction of a bidirectional traffic lane. Such a problem is typically encountered in rail traffic and personal rapid transportation systems with predefined and fixed traffic corridors. The proposed proportional-derivative (PD) fuzzy control algorithm, serving as a traffic control automaton, alternately assigns adaptive green light periods to traffic coming from each direction. The proposed fuzzy logic-based traffic controller has been compared with the conventional traffic control automaton featuring fixed-durations of green light intervals. The comparison has been carried out within a simulation environment for four different probability distributions of stochastic traffic flows at each end of the considered traffic corridor. Results have shown that the proposed fuzzy logic-based traffic controller performance is far superior to that of the conventional traffic control law in terms of achieving shorter vehicle queue lengths and less disparity in queue lengths for all considered simulation scenarios.