Traffic emissions at controlled intersections can be reduced when the possibilities of infrastructure-to-vehicle communication are put to good use. In this study the authors present two applications: one infrastructure-based and one vehicle-based that in concert are able to significantly reduce the overall emissions at a controlled intersection. The infrastructure-based application employs a model-predictive control approach, an advanced form of traffic-adaptive control. The vehicle-based application uses information gained from the infrastructure-based application (i.e. the estimated time that the vehicle approaching the intersection will be allowed to enter the intersection and cross the stop line) to improve a vehicle's approach towards an intersection. Both applications have the same aim: to avoid unnecessary accelerations, decelerations and delay. For both peak and off-peak hours, the results show that the application of a model-predictive controller as opposed to the more traditional traffic-actuated controller is beneficial both in terms of travel time reduction (∼15.5% in both cases) and carbon-di-oxide (CO₂) reduction (2.9 and 9.3%, respectively). Together with an approach advice the amount of CO₂ emitted in both cases can be further reduced with an additional 7%, assuming a 100% equipment ratio.

The effectiveness of electric vehicle (EV) on energy consumption reduction and traffic pollutant emission mitigation is highly expected for its high energy efficiency and zero-emission during running. However, there is a popular belief that the popularisation of EV is hindered by the cruising range limitation and the charging process inconvenience. The driving behaviours have an effect on EV energy consumption, which relates to the extending of EVs’ cruising range. Meanwhile, since the EV consumes lots of energy in urban road when approaching the traffic light, this study focuses on the study of EV eco-driving at signalised intersections with the support of cooperative vehicle infrastructure system technologies. First, by employing the chassis dynamometer test, a microscopic energy consumption rate model for different operation modes is presented. Then, based on driving behaviour analysis, an eco-driving model is established that provides velocity profile for EV eco-driving according to the current vehicle status information and traffic information, especially the signal phasing and timing information. Finally, the energy saving effect of the eco-driving model is fully evaluated with an example application. The results show that the eco-driving strategies advised by the proposed model have good performance on energy consumption reduction.

This study covers three main aspects. First, it presents a way to create driving cycles using only driver behaviour (acceleration, deceleration and maximum speed) and route data as input parameters. Second, a power-based approach to describe different vehicle architectures from internal combustion engine vehicle, over stop and start to series and parallel hybrid solutions is presented and respective component modelling approaches are introduced. Finally, the fuel consumption on a given cycle in function of eco-driving parameters is evaluated. It can be seen that hybrid solutions show minimum fuel consumption of about 3 L/100 km, whereas eco-driving habits do change slightly when applied to hybrid architectures because of new technologies such as braking energy recovery.

This study presents a method based on traffic microsimulation to support feasibility studies on charge-while-driving (CWD) systems for fully electric vehicles in urban environments. The examined CWD solution is deployed by charging zones (CZs), which are installed before the stopping lines at signalised intersections. The opportunity to charge an electric vehicle en route is provided for almost stationary vehicle conditions, when it may be in queue for junction control requirements. The analysed scenario refers to a 2 km urban arterial with eight signalised intersections, where 10% of the traffic is assumed to be electric vehicles. CWD performance results are reported from the viewpoints of both driver and energy provider. The estimated stop time for electric vehicles at any section can vary and is often below 30 s. However, the entire stop time for a vehicle along the arterial is higher: ∼50% of the vehicles can charge in a range of 10–65 s. For the energy operator's viewpoint, a support analysis for the CZ location was performed by observing the charging opportunities at various sections. Finally, the total electric power provided for the entire system is estimated.

Intelligent transport systems are accepted as an integral part of the transport system. They have high potential in reducing the carbon footprint of traffic while improving efficient and safe transport. The calculation of CO₂ emissions arising from the transport sector incorporating the impact of ITS is a challenging task. A systematic assessment methodology will support developers, public authorities and investors in ITS solutions to make sound decisions based on comparable and transparent impact estimates. As the basis for such an assessment, the fragmentation of traffic in underlying processes is suggested. These processes can be divided into transport demand related processes and driver behaviour and vehicle related processes. Together these processes lead to traffic flow. Transport processes are influenced by various factors. Both the processes itself and the factors influencing them can be affected by ITS. A systematic analysis of the potential effects of ITS on all these levels is the prerequisite for choosing a suitable modelling approach to quantify the effects. It also ensures the transparency of the modelling process by elucidating the required model sensitivities. The details of such an approach and its context from user need to a standardised assessment methodology for ITS is described.

Recently, logistics is not only playing more and more important role in social and economic development, but also caused serious energy consumption and environment pollution problems. Reasonable vehicle route planning is viewed as an important solution to reduce logistics enterprises’ operation costs as well as alleviate the energy and environmental problems. This study aims to propose a solution to time-dependent vehicle routing problem with time windows (TDVRPTW) considering minimising fuel consumption. First, a mathematical TDVRPTW model with the minimum fuel consumption as an objective function is established, in which the three-dimensional bin-packing problem is considered as a sub-problem and the alternative stop point concept is newly proposed to reduce the possible detouring distance and fuel consumption of logistic vehicle. Then, an ant colony algorithm is applied to solve the problem, and the departure time optimisation is introduced to further improve the obtained results. Finally, the proposed approach is evaluated with the real road network and traffic data of Beijing. The results show that the method introduced in this study outperforms the existing approaches in reducing fuel consumption as well as route length.

This study proposes a methodology for assessing the energy performance of railway service based on a micro-simulation of how trains run. In particular, the analysis focuses on the influence on energy consumption of three parameters, namely: line speed, stops frequency and timetable period. The performance assessment methodology aims to monitor the trend of two output variables: motion technique efficiency and specific consumption of the trains. The study has been developed on two levels. The first analysis concerns the assessing of the run of a single train; subsequently a timetable-based analysis is performed for a specific time window. At this level, it is also possible to consider the effects of regenerative braking on the entire consumption. This part of the article reports the results of run simulations and the related energy consumption of a regional train on a theoretical line, subsequently extending assessments to the timetable level. The second part describes the results of simulations of a run of the same train on a real line, as well as an analysis of the energy performance of a current timetable.

The improvement of risk management standards within the field of road safety has been one of the central goals of the EU funded project INCA (Integrated Nowcasting Through Comprehensive Analysis) Central Europe – Integrated Nowcasting system for the Central European area. The project approach was undertaken in a transnational and multidisciplinary way by enhancing the very short range weather model INCA for specific application fields (among others road safety). The communication between model developers, weather services, stakeholders and local authorities have been enhanced via intensified feedback loops. Meteorological parameters in INCA that are commonly used by road management authorities are 2-m temperature, surface temperature, precipitation and precipitation type. The INCA system has been put into pilot implementation in three different countries, that is, Austria, Slovakia and Slovenia. It has been shown that the system is able to offer detailed information about the temporal evolution and spatial distribution of snowfall and surface temperature, which is crucial for adequate short term decision making. The skill is especially high in the nowcasting range (which corresponds to a forecast horizon of ∼ 6 h) and the system is able to represent specific regional and local characteristics. An additional coupling of INCA with a road-specific energy balance model (like model of the environment and temperature of roads (METRo)) is able to further improve the skill and thus enhance the capability of INCA to be used in road management services.

Electroencephalogram (EEG) data are an effective indicator to evaluate driver fatigue, but it is usually disturbed by noise. The frequent head nodding, as well as the time of day and total driving time, also have very close relationship with driver fatigue. All these factors should be taken into account for comprehensive driver fatigue evaluation. 50 drivers are recruited to take part in the fatigue-oriented experiment on the driving simulator. Based on the EEG samples, the EEG-based indicator of driver fatigue has been established by artificial neural network. Subsequently, a new algorithm is present to compute the head nodding angle with posture data from the passive tools fixed on the driver's head and trunk, respectively, and then head-based indicator of driver fatigue is determined. Finally, a new evaluation model of driver fatigue is established with integration of four fatigue-based indicators with DBN (Dynamic Bayesian Network). The results show that it is more accurate to evaluate the driver fatigue compared with the sole EEG-based indicator.

A significant portion of the observed variability in roadway performance can be due to the difference and innate heterogeneity in drivers’ behaviour. Analytical models, stated preference data collection and studies and laboratory-based simulator experiments are developed to understand the driver behaviour for years. However, little has been done to fill the important gap between the survey/laboratory observed behaviour and the field observed behaviour. This study investigates drivers’ actual behaviour by conducting real-world field experiments in Beijing's roadway system. In the experiment platform developed, instrumented vehicles are employed for the advanced data collection and analysis in order to understand the impact of roadway category on drivers’ longitudinal behaviour, that is, car-following and car-approaching. These behaviour dimensions are identified in this study and quantified by parameters including relative speed, leading vehicle speed, accelerator release, braking activation, distance headway, time headway and time-to-collision. The analysis suggests that the drivers’ behaviour variation heavily depends on roadway characteristics, which supplements further theoretical and survey-based behavioural research. The research findings provide insight for theoretical advances, evaluating driving assistance systems and roadway-specific incentive designs for traffic harmonisation, speed reduction, collision warning/avoidance, safety enhancement and energy consumption savings.