Fuel cell electric vehicles (FCEVs) are perceived to be an intelligent transport choice for reducing emissions and are subject to considerable research and development as well as government incentives to accelerate deployment. However, though FCEVs produce zero tailpipe emissions they could, depending on the methods used to generate and distribute hydrogen, be more or less environmentally friendly than EVs or other alternatives. In this study, the authors review state-of-the-art FC technologies and identify the less polluting options. The environmental impact of FCEVs, pure battery EVs and internal combustion engine cars are then modelled, compared and forecast to 2050 under various scenarios. Finally, recommendations are made supporting solutions offering the most environmentally friendly way forward.

Nowadays, an ever-increasing number of information and communication technology solutions (hardware or software based) are finding their way to the automotive sector. Vehicles are being transformed into electronic hubs of information, communication, entertainment and other applications. Prior to commercial deployment, every single of these solutions must undergo a scrutiny of technical tests, often in the field (i.e. on-road as opposed to simulation), in order to ensure safe operation and robust performance. ‘Robustness’ is here perceived as operating as close to the target specifications as possible and with minimum variance, under varying conditions (factors). Meeting this requirement given a limited amount of resources (human, financial, equipment etc.) available for on-road technical tests is often a serious challenge for both researchers and product developers. This study proposes an experimental design process, based on suitable statistical means, for minimising the number of technical tests required to optimise the performance robustness of an automotive service or product under development. The process is substantiated and exemplified for the case study of an electric vehicle consumption estimation product, but could also be used in a variety of other applications (such as navigation, infotainment, safety solutions and others).

Contemporary traffic light control systems rely on sensors for detection of traffic which are costly in purchase, installation and maintenance. Emerging cooperative technology offers an attractive alternative where only one road side unit per intersection is required, instead of several infrastructure sensors per lane. However, studies showed that traffic control with cooperative detection requires a penetration rate of at least 20% to function effectively. To show the potential of cooperative traffic control, this study presents three algorithms: (i) the SWARM control algorithm, which is designed to work with very low penetration rates; (ii) an extension to the adaptive control algorithm, ImFlow, which uses cooperative data for enhanced queue modelling; and (iii) an ImFlow extension to stabilise green planning to enable green light optimal speed advice. The results from micro-simulation show a 7.8% improvement for stops and delay time over traditional adaptive control for Swarm, and 14.9% for Cooperative ImFlow. Adding planning stabilisation reduced the average perceived change for end users from 9.0–2.3%, without performance loss for the overall traffic flow. This shows the large potential of cooperative traffic control.

The formulation of data-driven short-term traffic state prediction models is highly dependent on the characteristics of collected data. Mobile sensors, specifically, on-board cellular phones (CPs) have proven success in wide scale real-time traffic data collection, in areas with limited traffic surveillance infrastructure. In this research, four short-term travel speed prediction models have been examined to cater the CP-based traffic data environment. Time-series concepts were adopted for speed prediction by autoregressive integrated moving average model and non-linear autoregressive exogenous model that is trained by neural networks. Alternatively, Bayesian networks (BNTs) and dynamic BNTs (DBNs) speed prediction models, from the graphical-based arena, have been investigated. The developed prediction models were tested in MATLAB environment on data from a simulation platform for 26-of-July corridor in Greater Cairo, Egypt. Testing results revealed the advantage of graphical-based models in restricting the propagation of prediction errors from one time step to the next. BNT reported a mean absolute percentage error (MAPE) of 6.31 ± 1.03, whereas the DBN model reported a MAPE of 5.34 ± 1.90.

The European Data Portal shows a growing number of governmental organisations opening up transport data. As end users need traffic or transit updates on their day-to-day travels, route planners need access to this government data to make intelligent decisions. Developers however, will not integrate a dataset when the cost for adoption is too high. In this paper, the authors study the internal and technological challenges to publish data from the Department of Transport and Public Works in Flanders for maximum reuse. Using the qualitative Engage STakeholdErs through a systEMatic toolbox (ESTEEM) research approach, they interviewed 27 governmental data owners and organised both an internal workshop as a matchmaking workshop. In these workshops, data interoperability was discussed on four levels: legal, syntactic, semantic and querying. The interviews were summarised in ten challenges to which possible solutions were formulated. The effort needed to reuse existing public datasets today is high, yet they see the first evidence of datasets being reused in a legally and syntactically interoperable way. Publishing data so that it is reusable in an affordable way is still challenging.

Car-sharing (CS) is an alternative to private cars able to change mobility customs and to participate in the greenhouse gas emission reduction. The administration board of the Belfort bus operator decided in January 2013 to build-up a visible and attractive CS service following a public service idea and integrated into the urban mobility offer, with significantly higher vehicle density and vehicle/inhabitants ratio than in the other French cities. This innovative approach resulted in a remarkably high ratio of nearly 42 members/1000 inhabitants in December 2016. In 2015, the service totalled >2300 clients, 33,000 rentals and 1.5 million kilometres after only 2 years running. These figures confirm the hypothesis that CS can be rapidly developed as a veritable alternative even in mid-sized towns. This article focuses on the design elements of the Belfort CS service as well as first analyses based on 2015 data and problems encountered.

Bicycle speed estimation is important for geometric design, traffic signal operations, microsimulation models, and health and safety assessment, among other applications. Bicycle speeds can vary greatly with the characteristics and power output of the rider and with travel conditions, especially road grade. This study presents a mathematical framework to address the non-trivial and practical problem of estimating bicycle free-flow speeds in a way that is sensitive to cyclist and roadway attributes. A closed expression is derived from first principles to determine speed from bicyclist power output. The method is extended to the problem of speed estimation for bicycles with limited gearing. Results are consistent with speed surveys in the literature. Application of the method to clearance interval calculation demonstrates the importance of context-sensitive bicycle speed estimation for advanced traffic signal systems.

Recent advances in combining deep neural network architectures with reinforcement learning (RL) techniques have shown promising potential results in solving complex control problems with high-dimensional state and action spaces. Inspired by these successes, in this study, the authors built two kinds of RL algorithms: deep policy-gradient (PG) and value-function-based agents which can predict the best possible traffic signal for a traffic intersection. At each time step, these adaptive traffic light control agents receive a snapshot of the current state of a graphical traffic simulator and produce control signals. The PG-based agent maps its observation directly to the control signal; however, the value-function-based agent first estimates values for all legal control signals. The agent then selects the optimal control action with the highest value. Their methods show promising results in a traffic network simulated in the simulation of urban mobility traffic simulator, without suffering from instability issues during the training process.

Traffic analysis road networks are extensively used in transportation planning and modelling practice. Due to computational complexity and burden, a traffic analysis road network is a subset network which usually selected from a full-size network. However, the process of subjectively choosing traffic analysis road network is problematic and may result in an unrepresentative road network which is useless for transportation analysis applications. This research targets on proposing a road network abstraction method that can scientifically and systematically select a representative road network from original full-size network to achieve both representativeness and computation efficiency in various transportation and traffic analysis applications. The road networks on dynamic traffic assignment and simulation model are the interests. At the same time, traffic analysis performance metrics, such as average travel time, vehicle routing choices, and volume, are chosen as the criteria to determine the abstracted network representativeness. A numeric experiment is conducted by implementing the method in a demonstrated Alexandria network scenario. The results indicate that the proposed method is very promising. The traffic analysis performance of the abstracted network is similar to the performance of the full-size network. However, the computational time of the abstracted network is significantly lower than that of the full-size road network.

The ability to identify driving risk status plays an important role for reducing the number of traffic accidents. Bayesian networks (BNs) was applied to extract the main factors that significantly influence driving risk status. Five factors (driver state, sex, experience, vehicle state, and environment) were selected and considered to significantly influence driving risk status based on driving simulation experiments. Next, a logistic regression algorithm was employed to establish the driving risk status prediction model, and the receiver operating characteristic curve was adopted to evaluate the performance of the prediction model. The area under the curve was 0.903, indicating that the prediction model was both adaptable and practical. In addition, this study also compared three different models, namely modelling directly, modelling based on expert experience, and modelling based on BN. The results indicated that modelling based on BN outperformed all other methods. The conclusions could provide reference evidence for driver training and the development of danger warning products to significantly contribute to traffic safety.