With the rapid development of Information and Communication Technologies, vehicular networks that communicate with each other will have an innovative application in traffic safety and congestion. This study describes a preliminary study on an automated on-ramp merging control algorithm for vehicles on freeways under condition of Internet-connected vehicles. On the basis of vehicular operation characteristics during the merging process analysis, a cooperative driving algorithm based on Internet of vehicles was designed to achieve ramp merging without collision. Then two on-ramp merging cases, including one vehicle and two vehicles merging into the platoon on main lane, were discussed in detail. Simulation works were carried out and the results proved that the on-ramp merging algorithm was effective, but the vehicle following the leading vehicle on ramp lane is disturbed seriously by the leading vehicle. At the same time, the simulation results also showed the scenario that merging a platoon into the two vehicles on main lane affects the traffic flow more seriously than letting each individual vehicle on ramp lane consecutively to merge in between the two vehicles in the main lane under the same initial condition.

Regarding the one-way restriction as a special case of the lane-allocation operation, this study formulates the general lane-allocation problem as a mathematical program with complementarity constraints (MPCC). Recognising the day-to-day and within-day travel demand variations, another counterpart of the MPCC is formulated to obtain a robust lane-allocation scheme that performs better against a set of worst-case demand scenarios. Solution algorithms based on active sets are proposed to address the problems by sequentially solving some relaxed-MPCC and a binary knapsack problem. Numerical examples are presented to validate the proposed model and algorithm.

Collaborative traffic-monitoring (CTM) systems exploit the location information continuously collected from vehicles. Location data are very sensitive information that made privacy a major obstacle for the widespread usage of CTM systems. The way how this data are generated and used is very important for users' privacy and data quality as well. Recently, two CTM approaches have been proposed, the first relies on a dedicated infrastructure that is called vehicular ad hoc networks (VANETs), and the second utilises the existing underlying infrastructure such as cellular and wireless networks. In this study, the authors propose a privacy aware collaborative traffic monitoring system (PA-CTM) that considers the privacy and security properties of VANETs and existing infrastructures. PA-CTM provides a client server architecture that relies on existing infrastructures and enhances privacy by: (i) Using a robust pseudonym providing system for anonymous access. Pseudonyms enable users to be authenticated anonymously by the traffic server without having their identities revealed. It also allows revealing identities for law enforcement purposes when necessary. Users are able to change their pseudonyms and hence hide their complete trajectory information form traffic server. (ii) Utilising a novel autonomous location update mechanism (ALUM) that does not rely on a trusted third party and uses only local parameters (speed and direction) for triggering a location update or pseudonym change. These performance results showed that ALUM is effective for traffic monitoring in terms of both privacy and utility.

An unconventional outside left-turn lane design has been increasingly used at signallised intersections in the urban areas in China. The unconventional outside left-turn lane design allows an exclusive left-turn lane to be located to the right of through lanes. The study presented in this paper evaluated the factors that affected drivers’ selection of left-turn lanes at signallised intersections where both inside and outside left-turn lanes were provided. A binary logit model was developed to quantitatively evaluate the impact of contributing factors on drivers’ selection of outside left-turn lanes. It was found that the chance of a driver choosing the outside left-turn lane would increase with an increase in the traffic volume on the major street and the queue length in the inside left-turn lane; and would decrease with an increase in the distance between the upstream side street to the signallised intersection. In addition, drivers of heavy vehicles and those from the upstream right-side side streets were more likely to select outside left-turn lanes. Based on the data analysis results, a procedure was proposed to help transportation professionals determine if outside left-turn lanes were needed given traffic conditions and geometric characteristics of a signallised intersection.

The performance of map matching has a significant effect on obtaining real-time traffic information. The floating car data (FCD) is of low-sampling rate, and urban road networks such as multi-layer roads can be particularly complex. Most of the current low-sampling-rate map-matching approaches use a fixed time interval, which can result in a lack of efficiency and accuracy if the initial point is not correctly matched. Moreover, the issue of handling data relating to multi-layer road networks remains open. To address these issues, a new on-line map-matching framework is proposed, comprising the confidence point and the maximum delay constraint dynamic time window. The framework performs map matching by self-adaptively choosing the appropriate timing and matching method according to the complexity of the local network to which the positioning point belongs. To distinguish elevated roads from normal roads, vehicle behaviour patterns on elevated roads are taken into account. Comparisons of the proposed algorithm, hidden Markov model algorithm, incremental algorithm and point-to-curve algorithm are conducted on two datasets. The empirical results show that the proposed algorithm outperforms the other algorithms. When the behaviour pattern on elevated roads is considered, the accuracy of these algorithms is also improved.

Studies on on-road behaviour imply that designing user-centred services is important for raising awareness about severe weather and adverse road conditions. Along with the developments of new communication technologies and practices, the research area of ITS is challenged to move on from traditional ways of collecting and distributing traffic weather information. This study presents two methods for potential improvements and personalisation of traffic weather information. The methods were demonstrated and evaluated by 440 respondents in Stockholm. Weather alerts were sent by SMS 12–48 h, up to a week, prior to the occurrence of severe weather events during 2008–2010. The service was personalised because of assumptions regarding perception and memory of weather, including user's recent observations. The second aspect of potential improvement was the introduction of a social network component, including user-generated local weather observations. The impact of the service was evaluated in a longitudinal study through a series of questionnaires on user behaviour and evaluation of the service. The combination of the two methods proved efficient as the amount of changed decisions was of considerable amplitude. A correlation between time of exposure and changed decisions implies that social components and interactivity may be a powerful tool in traffic weather services and ITS.

This study presents results from focus group sessions conducted in six countries, namely, Austria, Czech Republic, Finland, France, Portugal and Spain. The work was conducted within the framework of the European project INTERACTION, and the main aim was to collect the opinions of drivers that had cruise control and speed limiter systems available for more than 1 year. A constant comparison analysis was performed and content categories compared among the sessions. This technique allowed assessing whether themes that emerged from one group also emerged in another. Results provide insights into the motives underlying system use and their effects on driving behaviour. A higher percentage of drivers reported using cruise control more frequently than speed limiter. Moreover, drivers having both systems used cruise control significantly more than speed limiter. Statements of ‘dislike’ were documented for speed limiter in several group sessions. Although drivers had the systems available for more than 1 year, lack of knowledge regarding specific system functioning was apparent. Reports of trial-and-error learning in real driving conditions, experience of critical situations and performance of secondary tasks with both systems active are discussed in this study.

The effectiveness of signal coordination can be undermined by over-long spacing and different vehicle dynamic characteristics between neighbouring signal intersections in a signalised highway system. This study presents two dynamic speed guidance (DSG) strategies based on the traffic environment and facility deployment to congregate the traffic flow and optimise the arterial coordinated signal control system. One of them is the in-car display for individual guidance, and the other is the roadside display for group guidance. The two DSG strategies are calculated using the rolling horizon approach. A simulation platform that integrates data communication, traffic mobility and DSG application is programmed based on VISSIM, a microscopic simulation model. A section of Cao'an highway is used to assess the efficiency of two DSG strategies. The assessment results show that both the average stops and travel time of arterial vehicles can be optimised with the application of the two DSG strategies. In addition, the in-car strategy provides a significant benefit for on-board unit equipped vehicles, even in a low level of market penetration rate.

This study presents the applications of a pattern classifier named support vector machine (SVM) in predicting freeway sideswipe crash potential. Historical loop detector data for sideswipe crashes and corresponding non-crash cases were collected from Interstate-894 in the Milwaukee, Wisconsin, USA. Two sets of significant explanatory features were aggregated from the collected detector data to capture the prevailing traffic state and variances between adjacent lanes. Then, three SVMs with different nonlinear kernel function were formulated with the significant features as inputs. To comparatively evaluate the performance of SVM models against other commonly applied crash potential predictors, the multi-layer perceptron (MLP) artificial neural network models were also developed to predict sideswipe crash potential. The results showed that SVM models offers similar overall accuracy as the premier MLP model, but SVMs achieved better sideswipe crash identification at higher false alarm rates. The research also investigated the potential of using the SVM model for evaluating the impacts of traffic factors on sideswipe crash. Sensitivity analysis conducted on the trained SVM models successfully identified the variables’ impact on sideswipe crash. These results affirmed the superior performance of SVM technique in crash potential prediction analysis.

Information and Communication Technologies (ICT) rapidly migrate towards the Future Internet era, which is characterised, among others, by powerful and complex network infrastructures and innovative applications, services and content. An application area that attracts immense research interest is transportation. In particular, traffic congestions, emergencies and accidents reveal inefficiencies in transportation infrastructures, which can be overcome through the exploitation of ICT findings, in designing systems that are targeted at traffic/emergency management, namely Intelligent Transportation Systems. This study presents such a system that operates on the basis of collecting information from various sources (vehicles and infrastructure objects) through vehicular sensor networks, intelligently processing it, integrating knowledge and experience coming from the past and, finally, issuing directives to the driver for facilitating transportation. The overall approach is presented in detail, whereas a novel heuristic is proposed for the algorithmic process towards reaching decisions. Indicative simulation results showcase its efficiency, mostly with regards to proactively identifying a potential forthcoming danger and accordingly notifying the driver.

As a special case of mobile ad hoc networks, vehicular ad hoc networks (VANETs) have attracted great interest in the research community. With the help of wireless communication, many of the safety related and non-safety related applications can be realised in VANETs. In the multi-hop and dynamic topology networks, building and maintaining a route is very challenging. To disseminate information among the vehicles and the infrastructures efficiently, a position-based clustering multi-hop routing method is proposed. The method hierarchically organises VANETs based on the competitive learning Hebb neural network, which partitions the vehicles into clusters, and these clusters are represented by virtual cluster-heads. The method is evaluated using NS2 and compared with typical ad hoc routing protocol Ad hoc On-Demand Distance Vector, Distance Routing Effect Algorithm for Mobility. The simulation results prove that the method is efficient.

Bus regularity is a key element for high-frequency transportation systems: it represents a measure of service quality for both users and transit agencies. Therefore, evaluating the regularity is highly important, but may also be a complex task in medium-size cities, because of the huge amount of data, which must be collected and processed effectively. Automatic vehicle location (AVL) technologies can address the data collection problem, but they involve several challenges such as correcting anomalies in gathered raw data and processing information efficiently. In this study, the authors propose a methodology to handle AVL raw data in order to measure the level of service of bus regularity in each route direction of a transit network, as well as in every bus stop and time period. The results are represented by easy-to-read control dashboards. The authors discuss the experimentation of this methodology to provide a detailed characterisation of bus regularity. The methodology is applied to about 800 000 data records of the bus operator CTM in Cagliari (Italy).