Traffic Information Sources Research Articles

Commercial floating car data application in Sub-Saharan African transport planning contexts: A critical review and research agenda

Floating car data (FCD) are a source of traffic information reported by GPS-enabled probes from within the traffic stream. FCD define speed, travel time and movement patterns, all key inputs for transport planning. FCD are readily available over the entire road network without roadside sensors and communications infrastructure, making this a particularly relevant traffic data source in low- and middle-income countries (LMICs) where resource limitations restrict the expansive distribution of traffic sensors. This review article aims to investigate the applicable use-cases of FCD in LMICs, while simultaneously sensitising transport practitioners to the potential data biases that may be present in FCD. Particularly, this paper considers whether the context of Sub-Saharan African countries allows for the application of FCD similarly to high-income country FCD use-cases, critically examining insights from literature. Sub-Saharan African countries typically differ from high-income nations with respect to urban form, population distribution (and segregation), modal choice, driver population, and technology penetration. To date, there has been no literature that concatenates the current practice of FCD use-cases in Sub-Saharan Africa, nor defines the fundamental differences between transport and land use planning in high- and low-income countries with the view to determine if these dissimilarities impact FCD collection and use. Literature revealed novel research involving the application of FCD to investigate informal public transport systems that are specific to Sub-Saharan Africa, while also determining that accuracy and potential biases of FCD are not widely understood in Sub-Saharan Africa. Findings show that biases are likely to occur in FCD in Sub-Saharan Africa because of the representation and behaviours of the sample reporting FCD, as well as the urban form of Sub-Saharan African cities. The paper concludes by developing a research agenda that will assist to further the knowledge of FCD sample bias and the applicable use-cases of FCD in Sub-Saharan Africa.

Realistic traffic model for urban environments based on induction loop data

As we gradually move towards smarter cities, having greater control of the traffic in the city becomes of utmost importance. In addition, to efficiently manage such traffic, it is critical to be able to predict the impact of different traffic policies, and potential changes to the city road systems structure. To this end, accurate traffic simulation models must be derived that can help in this task. This paper presents a tool that aims to improve the representativeness of traffic simulations by generating realistic Origin-Destination (OD) traffic matrices. In particular, we focus on cities whose source of traffic information are the induction loop detectors deployed through the different streets and avenues of the city. By comparing against the widely used DFROUTER tool, part of the SUMO open-source traffic simulation package, we show how we are able to improve the traffic model accuracy significantly. Specifically, we achieved more realistic route lengths and a better distribution of traffic sources and destinations.

Effect of multiple traffic information sources on route choice: A driving simulator study

With the arrival of new technologies more en-route traffic information sources have become available, especially in-car information sources. The aim of this study is to gain more insight into the effect of multiple, and possibly conflicting, sources of information on route choice and driver behaviour. In a driving simulator experiment, participants were required to make multiple drives, each of which ended with a choice between the normal and an alternative route. On each trial participants received traffic information from a Variable Message Sign (VMS), i.e. a dynamic sign above the road providing descriptive traffic information in the form of expected travel times (ETTs), a navigation device providing in-car prescriptive route advice, or information from both sources. In the latter type of trial the information could be congruent or conflicting with regards to ETTs on the VMS and advise from the navigation. After each trial, participants indicated how much trust they had in the traffic information and their primary information source. A Bayesian model was used to quantify the propensity to switch to the alternative route. Results indicate that overall compliance was very high for the primary source even when the other source did not corroborate this information and that most participants preferred to use the information from a VMS. However, when both the VMS and the navigation device provided information and the VMS indicated the same ETTs for the normal and alternative route, route choice was influenced by the advice provided by the navigation device. Also, in this type of trial mean speed was significantly lower compared to trials in which the two sources were in conflict, indicating increased mental workload, most likely due to attentional dissonance: a situation in which stimuli compete for attention resulting in cognitive conflict and the need to inhibit non-relevant information. A deeper understanding of how drivers use multiple traffic information sources and cope with irrelevant information could support driver safety and comfort, increase the usability of information sources, and help reduce stress, anxiety, and information overload while driving.

Traffic accident detection and condition analysis based on social networking data

Accurate detection of traffic accidents as well as condition analysis are essential to effectively restoring traffic flow and reducing serious injuries and fatalities. This goal can be obtained using an advanced data classification model with a rich source of traffic information. Several systems based on sensors and social networking platforms have been presented recently to detect traffic events and monitor traffic conditions. However, sensor-based systems provide limited information, and may fail owing to the long detection times and high false-alarm rates. In addition, social networking data are unstructured, unpredictable, and contain idioms, jargon, and dynamic topics. The machine learning algorithms utilized for traffic event detection might not extract valuable information from social networking data. In this paper, a social network–based, real-time monitoring framework is proposed for traffic accident detection and condition analysis using ontology and latent Dirichlet allocation (OLDA) and bidirectional long short-term memory (Bi-LSTM). First, the query-based search engine effectively collects traffic information from social networks, and the data preprocessing module transforms it into structured form. Second, the proposed OLDA-based topic modeling method automatically labels each sentence (e.g., traffic or non-traffic) to identify the exact traffic information. In addition, the ontology-based event recognition approach detects traffic events from traffic-related data. Next, the sentiment analysis technique identifies the polarity of traffic events employing user’s opinions, which helps determine accurate conditions of traffic events. Finally, the FastText model and Bi-LSTM with softmax regression are trained for traffic event detection and condition analysis. The proposed framework is evaluated using traffic-related data, comparing OLDA and Bi-LSTM with existing topic modeling methods and traditional classifiers using word embedding models, respectively. Our system outperforms state-of-the-art methods and achieves accuracy of 97 %. This finding demonstrates that the proposed system is more efficient for traffic event detection and condition analysis, in comparison to other existing systems.

The Use of Dynamic Message Signs (DMSs) on the Freeways: An Empirical Analysis of DMSs Logs and Survey Data

This study evaluates the Dynamic Message Signs (DMSs) use to dissipate incident information on the freeways in Las Vegas, Nevada. It focuses on the DMSs message timing, extent, and content, from the operators’ and drivers’ perspectives, considering the variability in drivers’ freeway experience. Two-week incidents data with fifty-nine incidents, DMS log data, and responses from a survey questionnaire were used. The descriptive analysis of the incidents revealed that about 54% of the incidents had their information posted on the DMSs; however, information of only 18.6% of the incidents was posted on time. The posted information covered the incident type (54.2%), location (49.2%), and lane blockage (45.8%), while the expected delay or the time the incident has lasted are rarely posted. Further, the standard DMSs are the most preferred sources of traffic information on the freeway compared to the travel time only DMSs, and the graphical map boards. The logistic regression applied to the survey responses revealed that regular freeway users are less likely to take an alternative route when they run into congestion, given no other information is available. Conversely, when given accurate information through DMSs, regular freeway users are about 2.9 times more likely to detour. Furthermore, regular freeway users perceive that the DMSs show clear information about the incident location. Upon improving the DMSs usage, 73% of respondents suggested that the information be provided earlier, and 54% requested improvements on congestion duration and length information. These findings can be used by the DMSs operators in Nevada and worldwide to improve freeway operations.

Real-Time Traffic Congestion Information from Tweets Using Supervised and Unsupervised Machine Learning Techniques

For many decades automated sensors, such as acoustic, optical, pneumatic, magnetic, inductive sensors and video-based image processing solutions were used for collecting data on traffic flow, speed, density, travel time etc. Using these data, the level of congestion is indirectly estimated. However, most of these sensors are costly and location-based. In addition, they cannot find the reason for congestion. To address these limitations, use of social media as a source of traffic information has been explored recently. Some of them, such as Twitter, are open source and popular throughout the world. Earlier studies that reported the use of Twitter feeds for traffic information are limited and used machine-learning techniques based on supervised learning, which needs labelled data that are difficult to obtain. Hence, this paper compares the unsupervised and supervised approaches to extract traffic-related tweets and develops a method to give the probability of level of congestion in real-time. The methods developed were applied to the tweets from Chennai city to extract the congestion information in real time, the results were validated through newspaper reports of heavy traffic congestion events. The obtained results showed a good correlation with the real congestion events.

Information adoption in commuters’ route choice in the context of social interactions

The rapid development of information technology has significantly promoted social interactions among people. Social interactions may have become an important channel for commuters to obtain traffic information. Although commuters’ own travel experience and Advanced Traveller Information Systems (ATIS) are acknowledged as two common traffic information sources that can impact commuters’ route choice, the possible influence of social interactions has not yet been empirically confirmed. Therefore, an empirical study is conducted in this paper to explore whether and how social interactions affect commuters’ route choice. The presented study is divided into two phases. First, 1000 commuters are surveyed about their social interactions with other commuters. A total of 809 valid questionnaires are returned from the commuters. The survey confirms that social interactions among the commuters are very common and indeed influence commuters’ route choice. Second, because the essence of the influence of social interactions on commuters’ route choice is an information-adoption process, partial least squares (PLS) component-based structural equation modelling (SEM) is employed to study the key success factors that influence information adoption in commuters’ route choice in the context of social interactions. In the second phase, 236 valid questionnaires were returned. The obtained results demonstrate that information relevance, information accuracy, source expertise, source integrity, usefulness and extraversion are the factors that have significant influence on commuters’ information adoption in the context of social interactions; while the roles of responding, trust and field dependence are not significant. Finally, some discussion and inspirations are provided based on the data analysis results. The findings in the presented study offer some insights into using social interaction information to guide commuters’ route choice.

Examining the potential of floating car data for dynamic traffic management

Traditional traffic monitoring systems are mostly based on road side equipment (RSE) measuring traffic conditions throughout the day. With more and more GPS-enabled connected devices, floating car data (FCD) has become an interesting source of traffic information, requiring only a fraction of the RSE infrastructure investment. While FCD is commonly used to derive historic travel times on individual roads and to evaluate other traffic data and algorithms, it could also be used in traffic management systems directly. However, as live systems only capture a small percentage of all traffic, its use in live operating systems needs to be examined. Here, the authors investigate the potential of FCD to be used as input data for live automated traffic management systems. The FCD in this study is collected by a live country-wide FCD system in the Netherlands covering 6-8% of all vehicles. The (anonymised) data is first compared to available road side measurements to show the current quality of FCD. It is then used in a dynamic speed management system and compared to the installed system on the studied highway. Results indicate the FCD set-up can approximate the installed system, showing the feasibility of a live system.

A mixed-behaviour equilibrium model under predictive and static Advanced Traveller Information Systems (ATIS) and state-dependent route choice

In transportation planning and design studies it is customary to consider the network in its equilibrium state. Equilibrium conditions in the presence of Advanced Traveller Information Systems (ATIS) and recurrent congestion have been formulated either according to some behavioural principle derived from Wardrop’s assumption, or as fixed-point states of day-to-day dynamic assignment processes. In the latter approach only, a differentiation has been introduced between predictive ATIS and ATIS providing static information, but the impacts on equilibrium travel times are relatively unexplored. In addition, the route choice updating model is formulated without consideration of state-dependent effects, such as inertia to change. This forms the motivation for the present paper. According to traffic information sources, three classes of users are considered: (a) users with predictive information, (b) users with static information and compliant, (c) users not equipped with ATIS or noncompliant. Users of the three classes make choices in a stochastic manner based on a logit model subject to inertia. Users of the first class have lower perception variance in view of the higher information quality. The compliance rate is endogenous and dependent on the information accuracy of the static ATIS. The fixed points in the class-specific route flows of the dynamic day-to-day processes characterise a new concept of network equilibrium, referred to as Mixed User Equilibrium (MUE), where, if each user shifts from her currently used route to her newly chosen route, the observed class-specific route flows do not change. A variant of the method of successive averages is proposed for computing MUE. The model is illustrated by an example related to the Nguyen-Dupuis network. The impacts on equilibrium travel times of the market penetration of ATIS with different functionalities as well as of inertia are examined.

An operational framework to integrate traffic message channel (TMC)in emergency mapping services (EMS)

ABSTRACTTraffic Message Channel (TMC) is a technology for delivering traffic information to vehicle drivers. Sources of traffic information typically include police departments, Traffic Information Centers (TICs), camera systems, traffic speed detectors, floating car data, so on. Both public and commercial TMC services are operational in many countries. TMC messages can be also exchanged among TICs formally using standard protocols. The term emergency mapping (EM) encloses all activities finalized to produce geographic data on the consequences of a natural or man-made events. Emergency mapping services (EMS) normally exploit satellite or aerial imagery in order to extract the extent of the area affected and the impact on local infrastructures. In this paper, the authors verify the impact of the integration of TMC information in routinary EMS, exploiting the flood event occurred in November 2016 in north-west Italy as a case study. After an introduction to the problem and a description of TMC technology and of EMS, all technical aspects related to the integration of the two services are deeply described. Extracted traffic events have then been integrated and compared with EM products generated for the event previously mentioned. Finally, opportunities, limits, future enhancement and refining are summarized in the conclusions.

Social media based transportation research: the state of the work and the networking

Recently, there has been an increased interest in the use of social media data as important traffic information sources. In this paper, we review social media based transportation research with social network analysis methods.We summarize main research topics in this field, and report collaboration patterns at levels of researchers, institutions, and countries, respectively. Finally, some future research directions are identified.

Modelling travellers’ en-route path switching in a day-to-day dynamical system

ABSTRACTTravellers’ en-route path switching is a very common travel adjustment behaviour which can significantly influence the whole day-to-day network traffic flow evolution process. This study encapsulated travellers’ sequential path adjustment mechanism into a day-to-day dynamical system, to capture travellers’ en-route switching effect without actually building a complicated within-day model to go with. According to different path choice behaviours and traffic information sources, three traveller groups were contained in the dynamical system, including the conservative travellers, the adventurous travellers unequipped with advanced traveller information systems (ATISs) and the ATIS-assisted adventurous travellers. In the proposed day-to-day dynamical model, a fixed point problem was established for achieving the heterogeneous traffic flow caused by travellers’ en-route path switching. Some stability analysis of the proposed dynamical system was accomplished in the numerical experiment. The effect of en-route path switching behaviour was investigated and it was found to influence both the stability of the dynamical system and the average travel time of adventurous travellers.

Should Legacy Highway Advisory Radio and Citizens’ Band Radio Advisory Systems Be Continued?: Analysis of Traveler Usage and Satisfaction Regarding These Systems

Advanced traveler information systems (ATIS) are important to provide travelers with accurate, up-to-date travel information. With the advent of new ATIS technologies, such as smartphone applications, roadway agencies need to understand how travelers use existing ATIS technologies, such as the legacy Highway Advisory Radio (HAR). The University of Central Florida implemented three traveler surveys and one truck driver survey to obtain the opinions of Florida roadway users regarding HAR, the Citizens’ Band Radio Advisory System (CBRAS), and other traffic information sources. Travelers typically preferred dynamic message signs (32%), smartphone applications (23%), and vehicle navigation devices (21%) for general traffic information. Some 57% of travelers were aware of HAR, and 42% of these travelers had used HAR, with 84% of users expressing satisfaction with HAR. Additionally, 87% of travelers said that they would use HAR during emergencies, such as hurricanes. A tree model targeting user satisfaction was developed; the model showed that HAR users who thought HAR should be continued, or who would use HAR in emergencies, as well as more frequent HAR users, were more likely to be satisfied. Only 12% of surveyed truck drivers had ever used CBRAS, but these users tended to utilize CBRAS frequently, and 92% indicated satisfaction with CBRAS. Truck drivers preferred vehicle navigation devices (28%) and smartphone applications (22%) to receive traffic information. Even though these surveys indicate that new technologies are more popular than HAR and CBRAS, these older systems satisfy users and should be kept for redundancy; cellular communications may fail during emergencies.

Genetic Algorithm–Based Approach for Optimal Deployment of Roadside Units in Connected and Automated Vehicle Environments

Traffic management strategies play a critical role in connected and automated vehicle (CV/AV) environments in which vehicles will be the primary source of traffic information. In CV/AV environments, traffic data are collected from vehicles, and decisions are made at the transportation management centers (TMCs) and communicated back to vehicles via deployed roadside units (RSUs). This process poses a challenge at the early deployment stages of the technology because of the anticipated low market penetration. Under such conditions, RSUs must be optimally located throughout the network to provide continuity and expand the coverage of vehicle-to-vehicle communication systems. This study presents a genetic algorithm–based approach for determining the optimal locations of RSUs by maximizing the connectivity robustness measure, by taking into consideration vehicle clustering (groups of vehicles in transmission range of each other), network size, and other factors. Traffic simulation data were generated from a microscopic simulation platform (VISSIM) and used to test the proposed approach for different penetration rates. The results show that the proposed approach identified locations where more vehicles can communicate. The optimized RSU locations enabled communication between more vehicles in the network, which was identified by the increased robustness of connectivity. This aspect can maximize the amount of exchanged information between vehicles and the RSUs. Consequently, better traffic monitoring can be achieved by collecting more representative data of the traffic conditions in the network. Thus, optimal decisions, such as vehicle rerouting, are made at the TMCs and disseminated to as many vehicles as possible, which helps in achieving better management.

My Traffic Manager

Abstract We propose the idea of using the drivers themselves as sources of traffic information, providing a simple system interface on a mobile device to alert the user of possible traffic situations. Allied to this concept is a system, which based on information sent by drivers, identifies occurrences relating to traffic and again establishes communication with the drivers, this time to let them know these same conditions on their route. My Traffic Manager (MTM), cooperative system of traffic impact, aims to develop and provide a traffic information system that takes advantage of a collaborative network established between users, allowing the user to avoid roads with higher traffic flow, targeting the Android operating system and directly interacting with the existing Google Maps platform.

A VANET-Based A* Route Planning Algorithm for Travelling Time- and Energy-Efficient GPS Navigation App

Most current navigation devices or apps, based on the global positioning system (GPS), only adopt static information for route planning. Although some are equipped with an RDS-TMC receiver to receive real-time traffic events, many of these real-time traffic events are irrelevant to the vehicle. This paper offers three major contributions. First, a vehicular-ad-hoc-network- (VANET-) based [Formula: see text] ([Formula: see text]) route planning algorithm is proposed to calculate the route with the shortest travelling time or the lowest fuel consumption, depending on two real-time traffic information sources, which have not been used in traditional GPS navigation applications. The first traffic information source is the recorded traffic information of the road segment that the vehicle has passed through. It is further exchanged between vehicles through an IEEE 802.11p wireless link. The second traffic information is provided by Google Maps. A GPS navigation app is then implemented on the Android platform to realize [Formula: see text]. Finally, simulations for six route planning algorithms are executed by VANET simulator The ONE, in one congested and one noncongested time period, respectively. In summary, [Formula: see text] achieves significant reductions in both the average travelling time and fuel consumption of the planned route, as compared to traditional route planning algorithms.

Selection of Source and Use of Traffic Information in Emergency Situations

The access and the use of advanced traveler information systems (ATISs) by drivers during normal commuting have been extensively assessed and analyzed. Emergency managers and transportation officials have extended the results of studies of ATIS use under routine conditions to emergency scenarios under the assumption that drivers' responses to information under emergency conditions mimic those seen under normal driving conditions. A recent survey of potential hurricane evacuees suggests the need to revisit this assumption. Results indicate that although commercial radio reports and variable message signs continue to be the sources of traffic information cited the most frequently, other information sources (mobile phones, in-car systems such as Global Positioning System devices, and the Internet) have significantly increased in importance. Rapid growth in user rates and the relatively low cost of implementation suggest that a revision of plans for emergency transportation information communications may be warranted. Better, more effective use of ATISs during emergency situations, especially when traffic incidents occur, may lead to improved and more reliable travel times and improved safety and emergency response. With the use of factor analysis, four driver personalities are identified, with each one characterized by the proclivity for and response to traffic information. This information will be of interest to developers and users of ATISs and to those responsible for emergency management and transportation planning.

Real-Time Traffic Information and Navigation

Consumer demand for accurate, reliable, and up-to-date traffic information has prompted TomTom to develop a new system for using multiple traffic information sources and integrating them with in-car navigation. This new system, called high-definition traffic, has been introduced in the Netherlands, United Kingdom, France, Germany, and Switzerland and is planned for introduction in other European countries in 2009. The goal is to provide drivers with accurate door-to-door travel time, delay time, estimated arrival time, and seamless cross-border information services integrated into personal navigation devices: thus route choice can be made based on real-time traffic information and adjusted continually on the basis of changing traffic conditions. The traffic information provided to drivers is based on four sources: data from global system for mobile communications networks, data from users of TomTom navigation devices (real-time Global Positioning System data), third-party data such as government traffic control centers, and historic information collected from navigation devices. The information is provided to drivers by connected navigation devices, which include subscriber identity module cards and allow for two-way data transmission. These connected devices incorporate traffic information into route calculations to provide drivers with real-time travel and delay times. The combination of more accurate and complete traffic information with connected in-car systems provides drivers with the capability of making informed decisions about departure times and routes as well as whether to make a trip by car.

The perceived usefulness of different sources of traffic information to trucking operations

Abstract Managers in charge of the California operations of nearly 1200 trucking companies were asked their opinions regarding how useful various sources of traffic information are to their dispatchers and to their drivers. They were also asked to evaluate the usefulness of improved traveler information systems. Nonlinear canonical correlation analysis was used to link company characteristics and perceptions of the value of the sources. Results showed that evaluations of sources such as Internet traffic information, in-vehicle navigation systems, and area-wide dedicated highway advisory radio are primarily related to the location of operations, whether a trucking operation is private or for-hire, the average length of the company's load moves, and the provision of intermodal services.

Decision and Workload Implications of Free Flight and the Cockpit Display of Traffic Information (Cdti)

Twenty-two certified flight instructors flew a general aviation simulator, either actively selecting and flying maneuvers to avoid traffic conflict, using a cockpit display of traffic information (CDTI) in a free flight scenario, or more passively executing corresponding maneuvers as commanded by a simulated air traffic controller, in a conventional scenario. The maneuvers spontaneously chosen by the pilots in the freeflight scenario were found to be only partially consistent with the maneuvers for traffic avoidance, dictated by current FAA “rules of the road”, with pilots showing a marked preference for making vertical rather than lateral maneuvers, and climbing rather than descending maneuvers. Pilots also preferred to make simpler single axis maneuvers. Visual scanning in both scenarios was measured, revealing that pilots spent a majority of their time fixating on the instrument panel, and in free flight, they distributed their remaining visual attention differentially across the two sources of traffic information, the outside world and the CDTI. In all conditions, they were particularly vulnerable to missing the detection of traffic which was not annunciated on the CDTI, but was visible on the forward view.