Conventional cars allow their users to decide about a journey's modalities largely independent. Electric vehicles (EVs) also must ensure this to be a real substitute from the user's point of view. This study focuses on range as a crucial technical feature of EVs. Many of the existing studies underestimate range requirements, as they are based on (i) one-day analyses and on (ii) mean values of daily trip distances. These data are not appropriate to answer questions on user needs; longitudinal mobility data are more suitable. This study analyses both, existing mobility data and new recorded global positioning system (GPS) data with regard to range requirements. Particularly, it was analysed whether or not identified daily driving cycles can be fulfilled with EVs assuming different charging opportunities. The analysis shows that GPS-recorded driving cycles of 80% of small-scale vehicles, 67% of midsized vehicles and 40% of sport utility vehicles can be realised with EVs.

Studies that explore drivers’ decision behaviour under the graphical route information panels (GRIPs) via the discrete choice models have rarely been reported. In this paper, drivers’ route choice response to the GRIPs is quantitatively analysed by using the discrete choice models through a case study of a real-life GRIP in Shanghai, China. A logit model for predicting the route choice probabilities is developed to capture the relationship between the route choice probability, the driver attributes and the GRIP messages, respectively. The modelling results show that the drivers who have a larger annual driving mileage, access the traffic information via the electronic message signs in daily life highly value the GRIPs and drive their car mainly for the commuting purpose are more likely to divert from the original route to the alternate route under the GRIP; a driver's perceived delay of the original route has a positive effect on his diversion decision under the GRIP; there exist differences in the GRIP response behaviour; the drivers are more likely to divert when the GRIP displays ‘red’ to indicate severe congestion on the original route; the female drivers are more sensitive to ‘red’ and more likely to divert.

Infrared light emitting diodes (LEDs) with a half-intensity angle Φ_1/2 ≃ 10° are suitable for electronic-toll-collection (ETC) applications. However, the radiation patterns of commonly available low-cost commercial infrared LEDs are irregular to some extent, which result in difficulties to construct an infrared ETC system with a proper communication area. Most low-cost infrared LEDs with a half-intensity angle Φ_1/2 ≃ 10° exhibit a common irregularity in their radiation pattern – a notch in the central radiant direction. The adverse influence bccause of such irregularities can be reduced by a proper design of an LED array in the emitter. High attenuation for the infrared signal of some windshields is also a serious problem for infrared ETC systems, which leads to severe shrinkage of the communication area if the emitter has not been properly designed. In this study, the authors present some successful designs with the aid of an optimisation algorithm to adjust the mounting angle of each constituent LED. In spite of the aforementioned irregularity, the proposed design still exhibits good performance in terms of the ability to withstand high signal attenuation and extended communication area in the vehicle-travelling direction.

In recent years, the issue of vehicle road sharing has attracted growing attention from both researchers and operators, as a potential instrument to improve the sustainability of urban mobility or transport systems. Beside the general concept, different operational models, managerial and technological solutions have been developed, leading to a high diversification of possible vehicle sharing configurations. This heterogeneity entails a considerable complexity of the service design phase, though few academic contributions tackled this specific problem and most of the papers focused on the dynamics of adoption and use of the service itself. To fill such a gap, this paper aims to present the approach followed in the design phase of an electric vehicle sharing service for the city of Milano. The methodology adopted in this work is based on the idea that a vehicle sharing service needs to be configured to answer to specific mobility needs coherently with the characteristics of target customers. To explain this idea the methodology was articulated into four steps, which are reported in detail in this study: (i) mapping of mobility profiles and service performances, (ii) competitive analysis, (iii) development of the service configurations and (iv) development of the evaluation model.

A wheeled train uncoupling robot with four degrees-of-freedom has been developed to replace humans in the uncoupling task in a marshalling field for designating freight cars to different destinations. To successfully achieve the task in practical applications, the positioning accuracy of the robot is an important issue to be considered. Based on the kinematic model using Denavit–Hartenberg method, the matrix differential method is applied here to establish the static position and orientation error model. The impact of parameter errors upon the static pose error of the uncoupling manipulator is analysed. The flexibility of the robot's key components is taken into consideration to analyse its impact on the position and orientation error of the manipulator. The position and orientation error compensation is developed by using input motion planning method to improve the pose accuracy of the robot. Additional motions are added to each joint of the robot such that the uncoupling manipulator can generate a corresponding tiny perturbation, which is used to eliminate the positioning error, ensuring the uncoupling action is completed successfully.

This study focuses on the role of traveller information and associated technologies in supporting personal security in travel by public transport. It reports research undertaken via a workshop involving SWOT analysis and scenario planning and a series of expert interviews. These research activities created a baseline understanding of how personal security issues are currently addressed, and identified potential future issues and how they might be tackled. Information is a major source of confidence and reassurance when travelling and can greatly support perceptions of personal security in travel. There have been significant advances in recent years in the quantity of information available and in delivery mechanisms. However, significant issues remain, particularly in terms of information quality, its representation in the public realm and its ability to support the needs of users. The differences in the relationship between information requirements and related commercial imperatives is shown to be perhaps the critical factor in determining the alternative pathways and associated services, technologies and personal security outcomes which emerge under the different scenarios.

This study aims to provide a comprehensive review of bus rapid transit (BRT) systems as an effective alternative transport mode which will benefit urban transportation knowledge for creating a BRT system. BRT can be seen as a feasible solution to traffic congestion and is designed using delicate transportation design strategies, such as, dedicated lanes, adequate station design, master planning of service and operation, faster toll collection, up-to-date intelligent transportation systems applications. BTR systems will improve time, safety and cost-effectiveness by accomplishing the goals of increasing speed and ridership. Unlike rail, BRT is a flexible transit design system. BRT acceptance is determined with algorithms such as logistic modelling and ridership data. Optimum potential determination of a system is possible by looking at international and local studies examining BRT problems and strengths. Problems consist of conflicts with other transportation modes and population booms. Determining if a system will be used also heavily falls on the quality. Most transit systems are perceived eyesores and inefficient modes with over-congestion, these barriers can be overcome with proper design that gives a captivating addition to the community.

Traffic sensors serve an important function in obtaining traffic information. In this paper, a novel traffic sensor location approach is proposed to determine the maximum number of traffic flows by considering the time-spatial correlation. The problem is formulated as three 0–1 programming models to maximise the number of obtained flows under different cases. To solve these novel sensor location problems, an ant colony optimisation algorithm with a local search procedure is designed. Numerical experiments are conducted in both a simulated network and in the Sioux–Falls network. Results demonstrate the effectiveness and robustness of the proposed algorithm, which is believed to possess potential applicability in real surveillance network design.

This study compares the performance of statistical and Bayesian combination models with classical single time series models for short-term traffic forecasting. Combinations are based on fractionally integrated autoregressive time series models of travel speed with exogenous variables that consider speed's spatio-temporal evolution, and volume and weather conditions. Several statistical hypotheses on the effectiveness of combinations compared to the single models are also tested. Results show that, in the specific application, linear regression combination techniques may provide more accurate forecasts than Bayesian combination models. Moreover, combining models with different degrees of spatio-temporal complexity and exogeneities is most likely to be the best choice in terms of accuracy. Moreover, the risk of combining forecasts is lower than the risk of choosing a single model with increased spatio-temporal complexity.

The technique to forecast available parking spaces (APSs) is the foundation theory of parking guidance information systems (PGISs). This study utilises data collected on parking availability at several off-street parking garages in Newcastle upon Tyne, England, to investigate the changing characteristics of APS. Using these baseline data the research reported here aims to build up a short-term APS forecasting model and applies the wavelet neural network (WNN) method to the PGIS problem. After selecting optimal preferences, including training set size, delay time and embedding dimension, the APS short-term forecasting model based on WNN is built and tested using the real-world dataset. By experimental tests conducted using the same dataset, WNN's prediction performance is compared with the largest Lyapunov exponents (LEs) method in the aspects of accuracy, efficiency and robustness. It is found that WNN prevails through a more efficient structure and employs, barely half of the computational cost compared to the largest LEs method, which could be significant if applied to real-time operation. Moreover, WNN enjoys a more accurate performance, for its prediction average mean square error (MSE) is 6.4 ± 3.1 (in a parking building with 492 parking lots) for workdays and 8.5 ± 6.2 for weekends, compared to the MSE of largest LEs method, 18.7 and 29.0, respectively.

Improved urban mobility can be attained through more efficient vehicle usage and better road network utilisation, namely through increased vehicle occupancy and new operation modes. In this study, the authors focus on a dynamic and distributed taxi-sharing system that takes advantage of nowadays widespread availability of communication and distributed computation to provide a cost-efficient, door-to-door and flexible service, offering a quality of service similar to conventional taxis. This system has been evaluated following a simulation modelling approach, including a realistic and accurate replication of the taxi operation in the city of Porto using empirical data (real origin/destination data and average occupancy rates). Simulation results show improved performance in terms of reduced fares (up to 8%), reduced total travel distance (up to 9%) and smaller operation costs. Furthermore, they proposed that several trade-offs (e.g. service performance against passengers’ transit times) should be considered during the system deployment and operation. In this study, it was also shown that increased system penetration rate and demand level can even further improve the system performance.

This study investigates the impact of weather on bus ridership using smart card data collected in Fengxian, Shanghai. The ridership data are categorised into three representative groups by the cluster analysis. The ridership data for each cluster are further divided according to the four seasons. Twelve separate multiple linear regression models with four weather variables and two dummy variables are constructed and calibrated. All four weather variables, namely humidity, wind speed, rainfall and temperature are found to have statistically significant negative effects on bus ridership. The magnitude of the impact varies depending on bus route types, seasons and mode share characteristics. Our analysis provides a valuable case study on weather's impact on bus ridership and concludes that there is no one-size-fits-all conclusion about the relationships between weather attributes and bus ridership, and it is critical to investigate those relationships in different geographical contexts. The results of this study can be used not only for long-term transit policy making but also as a decision making tool for short-term ridership forecasting.