In the traffic incident management process, it is important to predict the potential duration of an incident as accurately as possible. This study presents a model for estimating and predicting different duration stages of traffic incidents occurring on urban expressways, based on the initial information of the Traffic Incident Reporting and Dispatching System of Beijing. The accelerated failure time hazard-based model is used to develop the estimation and prediction models, as well as considering the unobserved heterogeneity, time-varying covariate and relationship between consecutive traffic incident duration stages. The developed models show that there are a number of different variables which affect different traffic incident duration stages. The model test results show that the developed model can generally achieve reasonable prediction results, except for with shorter or longer extreme values. The developed model will aid in traffic incident management by providing timely duration prediction based on the initial information, as soon as the traffic control centre receives the traffic incident report.

This study describes the gender differences in driving and visual behaviour observed under a high mental workload. The impacts of performing a set of in-vehicle auditory tasks on the behaviour of 34 drivers were studied in an on-road experiment using an instrumented vehicle. The results show that female participants tended to drive more attentively in baseline driving than males, but they were also more affected by the higher workload. The latter effect was identified by an increase in steering wheel adjustments and a slightly lower auditory task performance. Females adopted a more conservative coping strategy to compensate for the higher workload, as identified by increased headways and more stable lateral control. By contrast, male drivers did not appear to be affected in the same way, but their eye movements revealed significant gaze concentration and less mirror-checking. This suggests that male drivers may be less aware of the impact of mental distractions on their driving performance and visual behaviour, and adopt a simplification strategy to cope with the extra workload. These gender differences in behaviours and coping strategies can be explained only through a combination of traditional measurements and drivers' eye movements, which provide a supplementary measure for understanding driving behaviour. Increased understandings of such gender differences may have significant implications for the design and safe operation of future in-vehicle technologies.

A method for assessing potential synergies among different sets of transport telematic services (TTSs) is suggested. An Intelligent Transport System enhances transport by delivering one or more TTSs. The ability to deliver multiple TTSs to address a wide range of stakeholder needs is gaining momentum, not only from a marketing perspective but also from a technological perspective. The total cost of TTSs can be reduced if they share functionalities (i.e., sub-services provided by telematic systems). We show how this synergy can be assessed with the help of clustering methods. Knowledge about possible synergies of functionalities is useful in the (re)design and eventual deployment of TTSs, especially when the underlying telematic systems are able to support multiple TTSs. To adapt the clustering method for this purpose, we suggest a mathematical formulation of synergy among functionalities of TTSs. By applying the method to a set of 32 TTSs, we obtain a cluster formation of these TTSs according to their synergy measures. Overall, the results suggest that the joint implementation of TTSs targeted toward some problem domains can lead to significant cost savings, for example, Road User Charging, Infrastructure Repair and Maintenance, and Information on the Transport of extra large goods for the management of road transport infrastructure.

Road accidents because of fog are relatively rare but their severity is greater and the risk of pile-up is higher. However, processing the images grabbed by cameras embedded in the vehicles can restore some visibility. Tarel et al. (2012) proposed to implement head up displays (HUD) to help drivers anticipate potential collisions by displaying dehazed images of the road scene. In the present study, three experiments have been designed to quantify the expected gain of such a system in terms of the driver's reaction time (RT). The first experiment compares the RT with and without dehazing, giving quantitative evidence that such an advanced driving assistance system (ADAS) may improve road safety. Then, based on a modified Piéron's law, a quantitative model is proposed, linking the RT to the target visibility (V _t), which can be computed from onboard camera images. Two additional experiments have been conducted, giving evidence that the proposed RT model, computed from V _t, is robust with respect to contextual cues, to contrast polarity and to population sample. The authors finally propose to use this predictive model to switch on/off the proposed HUD-based ADAS.

Travellers' mode switch behaviour with the presence of high-quality Smartphone delivered multimodal information (SMMI) seems to have rarely been addressed. This study investigated commuters’ en-trip mode decision about switching from ‘auto’ to ‘park-and-ride’ (P + R) under high-quality SMMI that provides travel time for both modes, delay for auto, cause of delay, P + R cost and comfort level of rail transit. It is based on a stated preference survey of Shanghai travellers. A binary logit model was developed to identify contributing factors that affect mode switching decisions. Results showed that SMMI can significantly influence mode choice and its impacts depend on traveller attributes, driver's previous experience, and level of service attributes. Statistically significant explanatory variables in the model are delay for auto, comfort level of rail transit, gender, education level, income, driving experience, driving frequency, main criterion of mode choice, owning an easy public transportation ride card, previous use of P + R, perceived value of existing real-time traveller information and frequency of using real-time traveller information. This study also developed a practical logit model that encompasses policy related explanatory variables to obtain policy implications for real application of SMMI services in Shanghai.

In the years to come, public transport (PT) will be called to play a significant role towards achieving the sustainable transport system objective set for the future in Europe and beyond. To this end, the quality, accessibility and reliability of its operations should be improved. In this context, the favourable treatment of PT means within the road network may have, among others, a significant contribution. Such treatment can be derived as a result of an appropriate design of the road network facilities and/or the employed signal control at the network junctions. To this end, several approaches have been proposed, and it is the aim of this study to review the state-of-the-art and -practice in such approaches, focusing mainly on those attempting to provide priority via appropriate adjustment, in real time, of the junctions' signal control.

Social media data now enriches and supplements information flow in various sectors of society. The question addressed here is whether social media can act as a credible information source of sufficient quality to meet the needs of transport planners, operators, policy makers and the travelling public. A typology of primary transport data needs, current and new data sources is initially established, following which this study focuses on social media textual data in particular. Three sub-questions are investigated: the potential to use social media data alongside existing transport data, the technical challenges in extracting transport-relevant information from social media and the wider barriers to the uptake of this data. Following an overview of the text mining process to extract relevant information from the corpus, a review of the challenges this approach holds for the transport sector is given. These include ontologies, sentiment analysis, location names and measuring accuracy. Finally, institutional issues in the greater use of social media are highlighted, concluding that social media information has not yet been fully explored. The contribution of this study is in scoping the technical challenges in mining social media data within the transport context, laying the foundation for further research in this field.

Transport is an important source of air pollution and greenhouse gas emissions. Although the applications of information and communication technologies (ICTs) for transport, also known as intelligent transport systems, are seen as having great potential to help reduce emissions from road transport, their exact impact on CO₂ emissions are uncertain for decision makers from government to industry. This uncertainty hinders the deployment of such applications. Therefore there is a need for a common evaluation approach to assess the CO₂ impact of ICT measures in a systemic and realistic way. In this study, a methodology framework to evaluate the impact of ICT measures on CO₂ emissions is explained. The methodology was developed within the European Union FP7 project Amitran. In particular, this study focuses on the outline and the framework architecture of the methodology as well as the required interfaces between the required models. The use of the methodology is demonstrated by applying it to a use case of dynamic traffic light systems. Finally, the efforts made to validate the methodology and make it accessible to users are explained.

Forward collision avoidance systems have shown to be a particularly effective crash-avoidance technology. Multi-vehicle tracking capabilities play an important role in the real-world performance and effectiveness of such systems. In order to effectively and accurately track vehicles in a moving platform and in complicated road environments, the authors proposed a multi-vehicle tracking algorithm based on an improved particle filter. First, the authors used a vehicle disappearance detection and handling mechanism based on the normalised area of the minimum circumscribed rectangle of particle distributions. This mechanism is used to verify whether a new target is a vehicle and can also handle the vehicle exit during the tracking phase. Next, an improved particle filter-based framework, which includes a new process dynamical distribution, allowed for multi-vehicle tracking capabilities was used for vehicle tracking. Finally, an effective occlusion detection and handling mechanism was used to address the significant occlusion between vehicles. The combination of these added improvements in the algorithm results in the enhancement of the vehicle tracking rate in a variety of challenging conditions. Experimental tests carried out from different datasets show excellent performance in multi-vehicle tracking, in terms of accuracy in complex traffic situations and under different lighting conditions.

Highway operators around the world are using automated vehicle identification (AVI)-based techniques as a technological input for travel time estimation on highways. Various AVI technologies provide various travel time measurement samples: some of them are able to identify only personal cars (e.g. tolling tags), while others provide mixed samples of all vehicle classes (e.g. license plate matching). As the adequate information on travel times should concern the personal cars, the influence of heavy vehicles (HVs) should be eliminated from the samples, which is not feasible with the use of existing travel time estimation algorithms. It was observed that also during congestion travel times of personal cars and HVs remain dispersed. The motivation for the present study was to introduce an algorithm that would be able to exclude the influence of slower HVs in travel time estimation for technologies, providing mixed samples of travel time measurements. This was achieved by the use of robust statistics. The results of the study could be used by all highway agencies and operators who are encountering problems with unreasonably extended estimations of travel times because of the presence of slow HVs in the traffic flow.

Variable message signs – which comprise a type of advanced traveller information systems – can affect driver behaviour, especially considering route choice. Hence, their operation is integrated in traffic management strategies for the mitigation of traffic congestion. This research explores the factors determining driver response to variable message signs (VMSs) in the city of Athens. A stated preference questionnaire survey is undertaken and discrete choice analysis is performed towards this aim. More specifically, a random-effect ordered probit model is estimated that provides insight on the contributory factors that influence driver propensity to divert, when provided with information on incident occurrence via VMSs. Message characteristics, that is, incident type, impact and suggestion for an alternative route, trip characteristics, such as vehicle type, as well as, driver characteristics, such as driver age and income, have been found to affect driver behaviour. Furthermore, appropriate models are also estimated for subsets of the driving population (considering gender and age) and specific similarities and differences between the population behaviours are identified.

Auditory warning of speeding behaviour is considered to be one of the most effective methods developed to reduce the accidents involving commercial passenger vehicles. Facing a complex, mixed traffic condition and a lot of risky driving behaviours in China, commercial passenger vehicles need an effective speeding warning system to reduce the high accident rate. Although many automobile manufacturers have installed the speeding warning systems on their vehicles, the styles of these auditory speeding warning systems are different, and few studies has been found to investigate the effectiveness of the auditory speeding warning systems for commercial passenger vehicles. Therefore this study is intent to fill such a gap to evaluate the effectiveness of three different sound-based speeding warning styles. In this study, thirty drivers qualified for driving the commercial passenger vehicles are recruited and then asked to drive for four 80-km field trips on an expressway in Wuhan, China. Driving behaviour is logged by a monitoring system and is monitored by two observers during these trips. Study results showed that ‘beep warning’ is most effective and ‘break-sound warning’ is the least. Basically, the results of this study could provide a good reference for development of future voice-based speed warning systems in China.