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This study sheds light on the travel behavior of drivers when they encounter fabricated messages in work zones. Using the response of 4302 participants to a stated preference survey, we develop a multivariate ordered response model and a structural equation model to study speed change and distraction response behavior. The results of our models for fabricated announcements signify that drivers normally follow the announcement and are affected likewise. The selected socioeconomic and attitudinal variables are shown to have mixed impacts in our speed and distraction models. Some variables are statistically significant for each model, while other variables are only statistically significant for one of the models. For instance, drivers that have seen a fabricated announcement before are less likely to speed up when encountering the message, while drivers who rely on technology for their daily travels are more likely to be distracted. Higher income is shown in our models to signify undesirable behaviors: speeding up and being distracted. Contrastingly, female drivers are less likely to do nothing or be distracted by the announcement. The findings, taken together, have implications for researchers and practitioners. First, they illustrate how cyberattacks can destabilize traffic in work zone and put the life of work zone crew members in jeopardy. Second, they explain the degree of compliance with compromised dynamic message signs.

It is anticipated that the prevalence of motorcycles in Asian countries will continue to increase, causing congestion and network imbalances concerning the nature of motorcycles. Literature demonstrates Variable Message Signs (VMSs) as an effective measure for addressing this issue. Understanding route choice behavior may thus aid in determining the appropriate traffic information to broadcast. This study aims to identify the impact of VMS messages related to traffic conditions and regulations on the route choice of motorcycle riders. In this instance, the core concept of ramp metering is adapted for non-highways to manage the proportion of motorcycles entering the traffic stream of the mainline. Two predetermined routes were offered through a stated preference survey to capture the responses to VMS. A binary logit model was initially introduced, further improved by including the individual characteristics and accommodating the unobserved factors across a series of observations (panel effects) by applying the mixed binary logit. It was revealed that traffic flow conditions significantly affect route preference; therefore, motorcycles tend to choose routes with lower volumes. However, waiting time at a ramp meter has no impact. The present research is a preliminary investigation for further implications in proposing traffic management strategies under mixed traffic situations.

In populated cities with high traffic congestion, traffic information may play a key role in choosing the fastest route between origins and destinations, thus saving travel time. Several research studies investigated the effect of traffic information on travel time. However, little attention has been given to the effect of traffic information on travel time according to trip distance. This paper aims to investigate the relation between real-time traffic information dissemination and travel time reduction for medium-distance trips. To examine this relation, a methodology is applied to compare travel times of two types of vehicle, with and without traffic information, travelling between an origin and a destination employing probe vehicles. A real case study in the metropolitan city of Tehran, the capital of Iran, is applied to test the methodology. There is no significant statistical evidence to prove that traffic information would have a significant impact on travel time reduction in a medium-distance trip according to the case study.

This paper deals with DATEX II profiling and validation aspects in the context of data modelling requirements coming from the European Commission’s Delegated Regulations. Since the DATEX II traffic data exchange standard has been established in many EU Member States, interoperability in terms of traffic data exchange even on an international level (between different European road operators) becomes more relevant in terms of enabling real-time based road management operations. Even though DATEX II is an open, commonly available standard for traffic data exchange, interoperability between two or more DATEX II systems can only be achieved having data elements commonly specified. With this respect, analyses of already existing DATEX II deployments have shown that there is a special need for harmonizing especially DATEX II generated data content profiles in order to ease bilateral traffic information exchange. Already when deploying DATEX II based traffic services, interoperability issues have to be considered from the very beginning, allowing smooth running traffic data exchange procedures. Therefore DATEX II profiling support is required in order to provide a harmonised exchange complying with different traffic data domains covering the Delegated Regulations as well. In detail a dictionary (middleware specification) providing a matching between Priority Action c (safety related), e (truck parking related) and b (real-time related) data categories and DATEX II data elements was elaborated within the CROCODILE project (co-financed by the Connecting Europe Facility) providing a minimum set of DATEX II elements to be used for harmonised profile generation. In order to allow DATEX II developers to check individually generated DATEX II profiles against a commonly defined minimum set of data (also referred to as ‘profile’), an online based DATEX II reference and validation tool was developed. The tool itself provides validation and parsing rules to be used for obtaining a high-quality compliance check. In other words, the testing tool (also referred to as DATEX II Test Center) provides a simple and harmonised procedure for validating individually generated DATEX II profiles against provided Delegated Regulation based traffic data domains and thus allowing basic profile consistency checks for harmonised data exchange. Based on the DATEX II Test center, DATEX II users and developers are able to check anonymously and voluntarily DATEX II profiles against predefined set of data elements to be commonly used. Thus a simple self-assessment is provided by the tooling.

Achieving the maximum utilization of the existing infrastructure is an essential goal of traffic management for both freeways and the urban road network. Reliable and explicit information about the current traffic condition are required for using the road network efficiently. So far, the lack of a uniform classification scheme results in a time consuming process of coordination between different stakeholders developing and implementing control strategies and providing traffic information. In this study, a uniform concept is developed defining the traffic conditions on freeways and urban roads. This classification scheme is applicable for all stakeholders (e.g. road authorities, broadcasting services, private service providers) and can be used in both infrastructure-based and vehicle-based applications. In order to determine an appropriate traffic flow parameter for a comprehensible representation of the traffic condition, extensive data from freeways and urban roads in Germany were collected and analyzed. The empirical basis is provided by segment-based and local data obtained with different detection methods. As expected, the average speed per segment leads to a more precise description of the traffic condition than local speed values from stationary detectors. Test runs with probe vehicles were made using a global position system (GPS) tracking device to record the floating car's driving speed. During the test runs, a subjective evaluation of the traffic flow quality was provided by different passengers (probands) in the vehicle. In total, over 15000 sets of travel speed data with associated grading of the experienced traffic flow quality were analyzed to develop a six-step classification scheme. Based on different approaches, threshold values of the average travel speed were determined depending on the type of road and the speed limit. This uniform classification scheme can offer valuable decision guidance for traffic management strategies. For providing more comprehensible traffic information for the road users, the developed scheme was transferred to a three-step system, easily illustrated by the colors green, yellow and red.

A Flash Crowd Effect (FCE) occurs when in the case of non-recurring congestion a large portion of drivers follows similar re-routing advice. Consequently, congestion is transferred from one road to another. Coping with the FCE is challenging, especially if the congestion results from a temporary loss of capacity (e.g. due to a traffic incident). The existing route guidance systems do not address FCE, as they either do not consider the effects of guidance on the rest of the road network, or predict link travel times based on the number of vehicles travelling on the link, which in the case of the loss of capacity is unreliable. We demonstrate that the FCE can be addressed in a distributed way with Vehicle-to-Vehicle (V2V) communication provided by Connected Vehicle (CV) technology. The proposed in-vehicle TrafficEQ system provides vehicles with mixed route guidance strategy—i.e. a route is autonomously chosen by the vehicle with a probability that is inversely proportional to the latest reported travel time on the route. Real-time travel time information is crowd-sourced by TrafficEQ users. Using realistic simulations of incident-related capacity drops on a classic two-route highway example and a realistic urban road network, we demonstrate that TrafficEQ can address the FCE by reducing travel time oscillations among the alternative routes. The system's drawbacks—in particular the occasional necessity of providing incentives to follow the guidance—are discussed.

In order to improve driving safety and minimize driving workload, the information provided should be represented in such a way that it is more easily understood and imposing less cognitive load onto the driver. Augmented Reality Head-up Display (AR- HUD) can facilitate a new form of dialogue between the vehicle and the driver; and enhance intelligent transportation systems by superimposing surrounding traffic information on the users view and keep drivers view on roads. In this paper, we investigated the potential costs and benefits of using AR cues to improve driving safety as new form of dialog between the vehicle and the driver. We present a new approach for marker-less AR Traffics Signs Recognition system that superimposes augmented virtual objects onto a real scene under all types of driving situations, including unfavorable weather conditions. Our method uses two steps: hypothesis generation and hypothesis verification. In the first step, Region Of Interest (ROI) is extracted using a scanning window with Haar cascade detector and AdaBoost classifier to reduce the computational region in the hypothesis generation step. The second step verifies whether a given candidate and classified into vehicle and non-vehicle classes using edge information and symmetry measurement to verify them. We employ this approach to improve the accuracy of AR traffic information system to assist the driver in various driving situations, increase the driving comfort and reduce traffic accidents.

The overall objectives of the TeleFOT project are to evaluate the impact of different functions provided by nomadic and after market devices (ND) on four impact areas: efficiency, environment, mobility and safety. The fifth area to be investigated is user uptake. The paper presents preliminary results from one of 13 field operational tests (FOTs), the Swedish L-FOT2 focusing on user uptake. One device and three functions were tested: a Green Driving Support System (GD), a Navigation Support System (NAV) and a Traffic Information Service (TI). Preliminary results indicate the importance of addressing the relative rather than the absolute benefits of a function, i.e. the benefits relative other functions offered by other channels/devices and, further, that the specific characteristics and contents of a function need to be analysed in depth order to understand user uptake.

In accordance with the features of variety and disparity of traffic information, using wireless sensor network to monitor traffic environment, this paper proposes an optimization algorithm of applying wireless sensor to monitor traffic environment. In this paper, to optimize the coverage, we analyze the irregularity of network sensing areas based on environmental factors and propose a wireless sensor network optimization algorithm. According to the irregularity of network node sensing areas, we build an irregular network coverage model, divide the node monitoring area to improve the network coverage and make corrections to the coverage according to the network coverage connectivity and other features. The simulation test proves that the method proposed in this paper can avoid too many redundant nodes - it only requires a reasonable number of network nodes. This will reduce network energy costs and increase network connectivity without affecting the coverage, thus improving the network performance. the algorithm can satisfy the requirements of real-time acquisition, processing and remote supervision of traffic information.

The main challenge for the pure electric vehicles (PEVs) with a hybrid energy storage system (HESS), consisting of a battery pack and an ultra-capacitor pack, is to develop a real-time controller that can achieve a significant adaptability to the real road. In this paper, a comprehensive controller considering the traffic information is proposed, which is composed of an adaptive rule-based controller (main controller) and a fuzzy logic controller (auxiliary controller). Through analyzing the dynamic programming (DP) based power allocation of HESS, a general law for the power allocation of HESS is acquired and an adaptive rule-based controller is established. Then, to further enhance the real-time performance of the adaptive rule-based controller, traffic information, which consists of the traffic condition and road grade, is considered, and a novel method combining a K-means clustering algorithm and traffic condition is proposed to predict the future trend of vehicle speed. On the basis of the obtained traffic information, a fuzzy logic controller is constructed to provide the correction for the power allocation in the adaptive rule-based controller. Ultimately, the comparative simulations among the traditional rule-based controller, the adaptive rule-based controller, and the comprehensive controller are conducted, and the results indicate that the proposed adaptive rule-based controller reduces battery life loss by 3.76% and the state of change (SOC) consumption by 3.55% in comparison with the traditional rule-based controller. Furthermore, the comprehensive controller possesses the most excellent performance and reduces the battery life loss by 2.98% and the SOC consumption of the battery by 1.88%, when compared to the adaptive rule-based controller.

This paper presents a freeway driving cycle (FDC) construction method based on traffic information. A float car collected different type of roads in California and we built a velocity fragment database. We selected a real freeway driving cycle (RFDC) and established the corresponding time traffic information tensor model by using the data in California Department of Transportation performance measure system (PeMS). The correlation of road velocity in the time dimension and spatial dimension are analyzed. According to the average velocity of road sections at different times, the kinematic fragments are stochastically selected in the velocity fragment database to construct a real-time FDC of each section. The comparison between construction freeway driving cycle (CFDC) and real freeway driving cycle (RFDC) show that the CFDC well reflects the RFDC characteristic parameters. Compared to its application in plug-in electric hybrid vehicle (PHEV) optimal energy management based on a dynamic programming (DP) algorithm, CFDC and RFDC fuel consumption are similar within approximately 5.09% error, and non-rush hour fuel economy is better than rush hour 3.51 (L/100 km) at non-rush hour, 4.29 (L/km) at rush hour)). Moreover, the fuel consumption ratio can be up to 13.17% in the same CFDC at non-rush hour.

Automobile congestions have an adverse effect in modern societies, causing the loss of billions of dollars and manhours every year throughout the world. In this era of global economic recession, drivers will require the necessary solutions and driving aids that facilitate the improvement of daily road transport and minimize unnecessary expenditure. In this work, we lay the groundwork for V-Radar, a query protocol for retrieving vehicular traffic information using V2V communications. The advantage of V-Radar over related works is its ability to monitor using location-dependent queries the prevailing traffic conditions in a number of road-paths from a vehicle's current location towards its final destination. We introduce its modular architecture and provide preliminary evaluation results showing significant improvements over a similar scheme. © 2012 IEEE. Conference code: 95174 Cited By :1

Providing travel time information to travelers on available route alternatives in traffic networks is widely believed to yield positive effects on individual drive behavior and (route/departure time) choice behavior, as well as on collective traffic operations in terms of, for example, overall time savings and-if nothing else-on the reliability of travel times. As such, there is an increasing need for fast and reliable online travel time prediction models. Previous research showed that data-driven approaches such as the state-space neural network (SSNN) are reliable and accurate travel time predictors for freeway routes, which can be used to provide predictive travel time information on, for example, variable message sign panels. In an operational context, the adaptivity of such models is a crucial property. Since travel times are available (and, hence, can be measured) for realized trips only, adapting the parameters (weights) of a data-driven travel time prediction model such as the SSNN is particularly challenging. This paper proposes a new extended Kalman filter (EKF) based online-learning approach, i.e., the online-censored EKF method, which can be applied online and offers improvements over a delayed approach in which learning takes place only as realized travel times are available.

This study presents a model for exploring the interactions between a transport market and a monopolistic market for advanced traveller information systems (ATIS). We examine the properties of the information market, and in particular, the relationship between monopoly information pricing and road system efficiency. From model experiments, we find that increasing information penetration will lead to negative external effects for the already informed drivers. Furthermore, monopoly information pricing is not the most attractive option from a system efficiency viewpoint, because of the relatively high price charged by the monopolist. Third, a user or a producer subsidy on the information market can help realise a second-best optimum of road use. Finally, relatively low uncertainty on the road and high information costs limit the monopolist’s profit on the information market, as well as relative system efficiency. While relatively inelastic demand for mobility negatively affects the monopolist’s profit, the relative social benefits from private information peak at intermediate demand elasticities.

This paper presents the findings of a survey conducted among callers of a San Francisco BayArea telephone information service. A Bay Area traveler information system, TravInfo, has beenin operation since September 1996. In April 1997, a survey of TravInfo callers was conductedover a two-week period. The paper compares the call making and demographic characteristics ofthose who asked for traffic information and of those who asked for transit information. Changesin travel behavior after the calls were made were also investigated. The key findings of the studywere that the people who called for traffic or transit information were satisfied with the service.About one-third of the people who made a trip after calling TravInfo modified their travelbehavior, of which the majority took an alternate route or changed their departure time. Therewas no significant difference between the call making characteristics and the demographicprofiles. A greater proportion of repeat TravInfo customers consisted of people who drive theirown vehicles and check on traffic conditions than of those who were inquiring about transitinformation.

This paper presents the final results of the Target Study conducted for the evaluation of the Travinfo Field Operational Test. Four waves of telephone surveys were conducted in 1997 and 1998 among commuters shortly after major incidents on a selected corridor. The case study corridor is a 16 mile segment of US 101 serving major cities between San Francisco and San Jose.The commuter surveys were aimed at an understanding of travel behavior when major incidents occur. The results of the surveys suggest that traveler behavior is not greatly affected by individual incidents causing delays of less than 15 minutes. Although a fair portion of commuters listen to radio traffic reports, they do not often modify their travel behavior in response. In general, commuters do not believe that changing their travel plans will result in shorter travel time. Commuters tend to believe that their normal travel plans are faster than or as fast as alternate plans; however, they value traffic information more for the intangible benefits, such as the ability to reduce anxiety or stress. A 12 minute delay for a 35 minute commute might not be a strong enough incentive to shift mode or switch route because alternatives would not necessarily offer shorter travel time when encountering congestion due to an incident.

Since traffic information is indirectly priced and experiences significant economies-of-scale, it can not be described by the classic demand model normally used to explain most goods. Therefore, this report focuses on the derivation of a demand model that describes the supply and cost mechanisms influencing the market for traffic information. This study includes a discussion of the history of the market, its economies-of-scale, and its potential for future development. In addition, recent developments of value-added-resellers, the differences which exist in the marketing of their products, and some factors influencing their success in the market for traffic information will also be discussed. Several suggestions for both the commercial broadcast stations and the public agencies that aim to maximize the total benefits of traffic information are presented.

Floating Car Data (FCD) applications are based on the collection of geo-localized information updates that are issued by vehicles roaming in a given area. These data are an essential source for traffic information and are widely employed by Intelligent Transportation Systems (ITS). We present and examine a hybrid networking architecture and protocol that are used for the efficient execution of such a collection process. We employ a VANET-based multihop dissemination logic to spread control messages and elect designated nodes. Those nodes are exploited to report vehicular data via LTE communications. The performance behavior of the proposed protocol is evaluated through the consideration of two real urban scenarios. In comparing with performance bounds that characterize the performance behavior attained by state-of-theart hybrid integrated VANET and LTE mechanisms, we show our approach to offer a substantial reduction in the traffic load rates induced over the LTE cellular radio access system.

The book addresses the context and experience surrounding the widespread development of technological tools and ICT platforms to support the emergence of Intelligent Transport systems. Section 2.4 explores the technical specifications and technologies for multimodal travel and traffic information services (TTIS). TTIS address fundamental needs of the individual travelers by providing better information about available travel options, while also providing effective means for traffic and transport authorities to pursue global objectives such as improved safety, overall transport efficiency and decreased environmental impacts.

Dynamic traffic simulation models are frequently used to support decisions when planning an evacuation. This paper focuses on limitations in the modelling of travellers’ behaviour with respect to traffic information and compliance to evacuation instructions. More specifically, we propose a model framework where the traffic simulation is executed only once (instead of many times within an iterative traffic flow convergence framework, e.g., yielding a user-equilibrium assignment). Within this one-time execution of the traffic simulation (or dynamic network loading procedure), travellers are initially assigned to their instructed route (and destination), yet may continuously update their destination and route during their trip – while accounting for the possibly disutility associated with non-compliance – thereby responding to the changing (traffic) conditions (but not anticipating these conditions, as otherwise assumed by an iterative userequilibrium assignment). This way, the realized departure time, destination and route decisions are a result of the trade-off that travellers make between complying with the prescribed travel behaviour and following their preferred travel behaviour (i.e., the travel decisions that would have been made in absence of an active evacuation plan). Also, this approach allows modelling full compliance, no compliance, and any state in between. The face-validity of the model characteristics are illustrated using a hypothetical test example. The results show the importance of capturing compliance and information levels in the traffic simulation model, as they have a large impact upon the evacuation efficiency., European Journal of Transport and Infrastructure Research, Vol 11 No 2 (2011)