Advanced Traveler Information Systems Research Articles

Modeling and simulation of network traffic flow evolution based on incomplete information feedback strategies in the ATIS environment

Considering both the high complexity of urban traffic flow systems and the bounded rationality of travelers, providing traffic information to all travelers is an effective method to induce each individual to make a more rational route-choice decision. Within Advanced Traveler Information System (ATIS) working environment, temporal and spatial evolution processes of traffic flow in urban road networks are closely related to strategies of providing traffic information and contents of information. In view of the day-to-day route-choice situations, this study constructs original updating models of the cognitive travel time of travelers under four conditions, including not providing any route travel time, only providing the most rapid route travel time, only providing the most congested route travel time, and providing all the routes travel times. The disaggregate route-choice approach is adopted for simulation to reveal the relationship between the evolution process of network traffic flow and the strategy of providing traffic information. The simulation shows that providing traffic information to all travelers cannot improve the operational efficiency of road networks. It is noteworthy that an inappropriate information feedback strategy would lead to intense variation in various routes traffic flow. Compared with incomplete information feedback strategies, it is inefficient and superfluous to provide complete traffic information to all travelers.

Comparing paradigms for strategy learning of route choice with traffic information under uncertainty

Travellers route choice dynamics with endogenous reliability of ATIS are modelled.Joint strategy fictitious play model best describes route choice learning process.In case of low accuracy of information, choice behaviour tends towards randomness. This paper aims to model the traveller's day-to-day route choice in the case of an Advanced Traveller Information System (ATIS) through two learning paradigms: reinforcement-based and belief-based. The reinforcement learning approach is adopted in both a basic version and an extended one. Similarly, the belief-learning approach is adopted in both a Joint Strategy Fictitious Play version and in a Bayesian-learning one. All the models are compared and validated based on data collected by means of a stated preference experiment. The models explicitly account for the reliability of the information system, as this interacts with the inherent dispersion of network travel times and determines the overall level of uncertainty affecting the travellers adaptive learning behaviour. The experiment is then designed to simulate different levels of reliability for the ATIS. Results show that for intermediate and high levels of information accuracy, joint strategy fictitious play best predicts the respondents route choice behaviour under information provision, suggesting that a best-reply strategy is used by travellers for their route choices. In low information accuracy, the result suggests the payoff variability moves the choice behaviour toward randomness. The proposed approach provides useful tools to model travellers adaptive route choice behaviour and contributes to the support of effective ATIS design.

Effects of the amount of feedback information on urban traffic with advanced traveler information system

In a real traffic system, information feedback has already been proven to be a good way to alleviate traffic jams. However, due to the massive traffic information of real system, the procedure is often difficult in practice. In this paper, we study the effects of the amount of feedback information based on a cellular automaton model of urban traffic. What we found most interesting is that when providing the traffic information of a part of a road to travelers, the performance of the system will be better than that providing the road's full traffic information. From this basis, we can provide more effective routing strategy with less information. We demonstrate that only providing the traffic information of about first half road from upstream to downstream can maximize the traffic capacity of the system. We also give an explanation for these phenomena by studying the distribution pattern of vehicles and the detailed turning environment at the intersections. The effects of the traffic light period are also provided.

A day-to-day route flow evolution process towards the mixed equilibria

This study investigates a travelers’ day-to-day route flow evolution process under a predefined market penetration of advanced traveler information system (ATIS). It is assumed that some travelers equipped with ATIS will follow the deterministic user equilibrium route choice behavior due to the complete traffic information provided by ATIS, while the other travelers unequipped with ATIS will follow the stochastic user equilibrium route choice behavior. The interaction between these two groups of travelers will result in a mixed equilibrium state. We first propose a discrete day-to-day route flow adjustment process for this mixed equilibrium behavior by specifying the travelers’ route adjustment principle and adjustment ratio. The convergence of the proposed day-to-day flow dynamic model to the mixed equilibrium state is then rigorously demonstrated under certain assumptions upon route adjustment principle and adjustment ratio. In addition, without affecting the convergence of the proposed day-to-day flow dynamic model, the assumption concerning the adjustment ratio is further relaxed, thus making the proposed model more appealing in practice. Finally, numerical experiments are conducted to illustrate and evaluate the performance of the proposed day-to-day flow dynamic model.

Developing architecture of a traveler information system for dynamic equilibrium in traffic networks

Having criticized the current architecture of Advanced Traveler Information Systems (ATISs), this work discusses a new base of requirements to develop a new paradigm for traffic information systems. It mainly integrates three dimensions within a traffic system; drivers’ pattern of behavior and preferences, urban traffic desires, and capabilities of traffic information service providers. Based on the above, functional segments from several related backgrounds are brought together to structure a new architecture, called Interactive Traveler Information System (ITIS). The main interactive feature of this new architecture is a two-way communication track between drivers and the traffic information system provider—in fact, a decision on choosing a road at a particular time for an individual will be made based on the utility of both sides. This new configuration consists of driver-side smartphone application, centric traffic prediction, and decision-maker units, which will shape a new approach of decision-making processes. These all work together to satisfy the designated goal of ITIS that it is preserving the Wardrop equilibrium condition in the traffic network level. Finally, we concentrate on a comparison study, which shows a differentiation between performance of the proposed ITIS and the current ATIS model in a real situation. This has been done with simulations of analogical scenarios. The most noticeable advantage of the proposed architecture is not being limited to a saturation limit, and the positive effect of increasing system penetration in the performance of the newly introduced information system. In conclusion, new research subjects are suggested to be carried out.

Modeling of the dynamic flow propagation of multiple units of information under vehicle-to-vehicle communications based advanced traveler information systems

ABSTRACTIn a vehicle-to-vehicle (V2V) communications-based advanced traveler information system (ATIS), the dynamic flow propagation of multiple units of information depends on the interactions between the traffic and inter-vehicle communication characteristics and constraints, and the consequent dynamics. This study models the dynamic flow propagation of multiple units of information using a multi-layer framework that captures the dynamics of the three interacting layers: physical traffic flow, inter-vehicle communication, and information flow. The traffic flow dynamics are captured using a cell transmission model in the physical traffic flow layer. The inter-vehicle communication layer uses the time-dependent locations of vehicles in the traffic flow layer and inter-vehicle communication-related constraints to determine the occurrences of inter-vehicle communication. The information flow layer depicts the flow of multiple units of information as a network. Thereby, the proposed framework describes how the dynamic flow propagation of multiple units of information can be mapped from the traffic flow dynamics and the inter-vehicle communication constraints. Synthetic experiments analyze the interactions between the traffic flow dynamics and inter-vehicle communication constraints, and the flow propagation characteristics of multiple units of information. They illustrate that the proposed multi-layer framework enables the integration of the traffic flow dynamics and inter-vehicle communication constraints to generate insights into the flow propagation of multiple units of information. Also, they indicate that it can be extended to incorporate the dynamic flow propagation characteristics of multiple units of information into the design of robust V2V-based ATIS architectures.

Effective long-term travel time prediction with fuzzy rules for tollway

Advanced traveller information system is an important intelligent transportation systems application area, which provides information to transport users and managers in order to improve the efficiency and effectiveness of the transportation system, in the face of increasing congestion in urban cities around the world. So far very limited research attention has been focused on long-term travel time prediction (i.e. predicting greater than 60 min ahead). Long-term travel time forecasts can play a critical role in journey planning decisions for both private road users and logistics operators. In this paper, we have considered a fuzzy neural network incorporated with both imprecise and numerical information and developed a hybrid long-term travel time prediction model, which shows the better prediction capability than naive methods and highlights the importance of different data variables. The model combines the learning ability of neural networks and the knowledge extraction ability of fuzzy inference systems. The model was validated by using travel time data compiled from electronic toll tags on a 14 km length section of the CityLink tollway in Melbourne, Australia. The validation results highlight the ability of the fuzzy neural network model to accommodate imprecise and linguistic input information, while providing reliable predictions of travel times up to a few days ahead.

The impact of travelers’ rationality degree heterogeneity in the Advanced Traveler Information System on the network traffic flow evolution

The interplay between traffic information, which is normally distributed by the Advanced Traveler Information System (ATIS) and travelers’ decision behaviors, is prone to lead to high complexity in the evolution process of network traffic flow. Considering the obvious heterogeneity that is reflected in the numerous ways that travelers adopt ATIS information and choose routes, the lognormal distribution is adopted to describe the heterogeneity of travelers’ rationality degree. Introducing habitual factors of traveler route choice, modeling ideas of Multi-Agent and Mixed Logit are utilized to construct the day-to-day evolution model of network traffic flow, which is based on the value difference of travelers’ cognitive travel time. Furthermore, an integrated simulation algorithm based on the Monte Carlo method is specially designed to solve the previous evolution model. The simulation indicates that a lower individual difference and a higher rationality degree would lead to a more obvious aggregation phenomenon of network traffic flow and inefficiency of operation in road networks.

The Economics of Information in Transport

Travelers often are incompletely informed about travel alternatives, which has important implications for various domains of travel behavior such as whether or not to make a trip, modal choice, the timing of a trip or route choice. During the last decade large efforts have been made to increase the availability of information to travelers by means of advanced traveler information systems (ATIS). This paper reviews economic aspects of information in transport markets. First, I will discuss information acquisition from an economics perspective by characterizing costs and benefits of information, leading to the formulation of optimal strategies to acquire information. This will be done in the context of search strategies leading to sequential information acquisition. I further discuss the broader consequences of information acquisition on the functioning of transport networks. In congested networks, when travelers change their behavior on the basis of information they obtain, this will have consequences not only for their own travel times, but also those of other travelers. This leads to interesting positive (and possibly negative) spillovers having important policy implications. The next step is the analysis of a traveler's choice whether to adopt advanced traveler information systems, implying the derivation of a demand function for information.

Traveler-Oriented Advanced Traveler Information System based on Dynamic Discovery of Resources: Potentials and Challenges

An Advanced Traveler Information System (ATIS) aims at providing travelers with multi-modal trip planning, route guidance services and advisory functions. Most ATIS’s provide travel solutions that cover only specific geographic areas, travel modes and/or transport operators, and are insufficiently personalized. Moreover, the solutions are normally constructed upon a fixed set of resources (e.g. data sources and services), and thus are not highly adaptable to travelers’ diverse needs. Considering numerous existing ATIS’s and other resources on the Web, our vision is to use them complementarily to improve the adaptability, completeness, and personalization of the solutions. We envision an ATIS design, called traveler-oriented ATIS, that discovers and exploits web resources in an integrated manner to construct travel solutions tailored to travelers’ needs. In this paper, we discuss common approaches to managing resources, upon which the solutions are built, employed in various ATIS’s and their limitations that lead to our vision. Challenges of the vision are investigated, and a possible approach to address them is presented along with potential applications of Semantic Web and Multi-Agent Systems in the approach to demonstrate a promising direction to concretize the traveler-oriented ATIS design.

Fixed-Point States of Day-to-Day Assignment Processes with State-Dependent Route Choice

Abstract Stochastic User Equilibrium (SUE) has been proposed to overcome the limitations of the perfect knowledge assumption of classical Wardrop’s user equilibrium. Imperfect knowledge of the network and the associated heterogeneity in the perception of route travel times, as it occurs in the absence of Advanced Traveller Information Systems (ATIS), justifies SUE providing strictly positive flows for all routes. SUE is at the same time the steady state of a day-to-day Markov assignment process under the assumption of state independent route choice, i.e. choice independent of the previously chosen route. Still in a context of absence of ATIS, state-dependent route choice represents a more realistic assumption: users exhibit inertia to change. Experimental evidence supports the assumption. The paper investigates the fixed-point states of Markov assignment processes with state-dependent route choice. These fixed points characterise a new concept of network equilibrium, referred to as State-Dependent Stochastic User Equilibrium (SDSUE), where if each user shifts from her current route to her newly chosen route the observed route flows do not change. The existence of SDSUE is guaranteed under usually satisfied conditions. A modified method of successive averages is proposed for computing SDSUE. An example related to the Braess network with logit route choice illustrates the theoretical insights. Numerical evidence is provided of a symmetry property of SDSUE transition flows whereby, at equilibrium, the number of users shifting from route i to route j equals the number of those shifting from route j to route i.

How Weather Events Affect Freeway Demand Patterns

The ability to estimate freeway traffic demand during inclement weather events is critically needed for demand and supply management, level-of-service assessment, capacity verification, and other freeway operational and planning applications. Reliable travel demand prediction is also a useful input for freeway travel time reliability analyses and advanced traveler information systems. This paper investigates the effect of inclement weather on freeway demand with the use of hourly traffic and weather data collected for 5 years (2011–2015) at 15 urban freeway locations in the city of Milwaukee, Wisconsin, which represents a North American region experiencing distinct seasons with significant snowfall and cold temperatures in the winter. About 489,300 hourly traffic demand observations were used in the analysis, and more than 52,800 of those were under inclement weather. Freeway demand reduction factors were introduced for diverse weather variables, precipitation intensities, visibility levels, temperatures, weekdays and weekends, and different times of day. Variability across seasons and sites was also examined. The paper further explores traffic demand in the hours immediately preceding or following inclement weather events. Finally, freeway weather-related demand and capacity adjustment factors are compared. Lessons learned from the study along with recommendations for future Highway Capacity Manual editions are summarized.

Advanced Traveler Information System: Itinerary Optimization as an Orienteering Problem Using Iterative Local Search-Hill Climbing Algorithm

Traffic congestion is a problem that becomes government concern in many big cities like Surabaya. Some initiatives have been carried out by the government to decrease the intensity of traffic congestion, ranging from various policy like special lane for motorcycle, odd – even license plate, etc. However, it is yet to be successful to decrease the heavy traffic in many big cities in Indonesia. The right solution tends to encourage people to use public transport instead of private cars. Within the framework of Intelligent Transport System, this paper proposes an advanced traveller information system with itinerary optimisation by Angkot, a common public transport mode in Indonesia. The itinerary optimisation is modelled as an orienteering problem and is solved using iterative local search – hill climbing algorithm. The experiment results show that generally the proposed algorithm could solve the problem fast and effectively. In addition to propose the initial algorithm, the main contribution of this paper is a new dataset for orienteering problem that may encourage researcher to come up with more sophisticated algorithm. Eventually, the proposed system could encourage more people to use public transport.

A multimodal transport network model and efficient algorithms for building advanced traveler information systems

Abstract: Route planning in urban public transport systems constitutes a common decision problem faced by travelers. Therefore, building Advanced Traveler Information Systems (ATIS) that provide passengers with pre-trip information on navigating through the network has become a certain need. Since passengers do not only seek a short-time travel, but they endeavor to optimize other criteria such as cost and effort, an efficient routing system should incorporate a multiobjective analysis for both routes and transport modes. We propose in this paper a new formulation that adequately allows representing a public transit network, as well as, yielding correct results when applying routing algorithms. Based on this formulation, we develop a multicriteria routing algorithm to determine the entire set of nondominated solutions to solve an itinerary planning problem. We introduce also several enhancement strategies to accelerate the algorithm's search process. As transportation modes, we focus on Railway, bus, tram and pedestrian. As optimization criteria, we use travel time, number of transfers and the total walking time. Experimental results have been assessed by solving real life itinerary problems defined on the transport network of the city of Paris and its suburbs. Results indicate that test problems were solved within reasonable amount of time and the new approach is efficient enough to be integrated within a real world journey-planning system.

Outlier filtering algorithm for travel time estimation using dedicated short‐range communications probes on rural highways

With increasing market penetration, dedicated short-range communications (DSRC) probes are attracting more interest in the advanced traveller information system as a way to efficiently alleviate traffic congestion. Generally, DSRC probes, thanks to their ability to directly collect point-to-point travel time, are considered to be superior to conventional point detectors. However, outlying observations are inevitable in DSRC probe data, and can erroneously indicate abnormally long travel times. In this study, a practical algorithm to filter out outliers in DSRC probe data is proposed. The suggested algorithm is divided into two parts. In the case of small sample, it uses a previous interval value to determine a valid range for current interval values; otherwise, it uses a modified median filter that uses the median and absolute deviation of current interval observations to determine the valid range. The algorithm has been thoroughly verified using various types of DSRC probe data in interrupted and uninterrupted rural highways near Seoul, Korea. Consequently, it was proven to be sufficient to overcome the deficiencies of the previous techniques.

Stream travel time prediction using particle filtering approach

Travel-time information is an integral part of Advanced Traveler Information Systems and Advanced Traffic Management Systems. Real-time estimation of stream travel time will be helpful in making trip decisions such as route choice and departure time. The present study proposes a method to predict stream travel time using particle filtering approach which considers the predicted stream travel time as the sum of the median of historical travel times, random variations in travel time over time, and a model evolution error. The present model hypothesizes the median of historical travel times obtained from the collected data as a priori estimate and hence predicting the actual travel time will be equivalent to forecasting the variations in travel time according to the current measurements. In order to capture the random variations in travel time, a dynamic mathematical modeling approach with particle filtering technique is used. The results obtained from the implementation of the above method are compared with the measured travel time data and the prediction accuracy, quantified using the Mean Absolute Percentage Error and Mean Absolute Error was observed to be satisfactory. Performance of this method was compared to a basic speed-based approach, where travel time is obtained by time–distance relationship using space-mean speed, and the proposed method showed better performance.

A study of users' acceptance and satisfaction of emergency call service

SummaryIn recent years, there has been rapid and significant development of road transport technologies in order to reduce the number of killed and injured people on roads. These include safety technologies, emergency call systems (eCall), and advanced traveler information systems. The eCall system is an automatic in‐vehicle emergency call service, which is mainly used for notifying emergency services about dangerous road situations and their exact location. In this paper, an eCall platform prototype is developed to allow quick and efficient rescue of injured people in dangerous road situations. The eCall function is developed and installed in nomadic devices (e.g., smartphones and tablets). Large‐scale field operational tests were conducted in real settings to assess the impacts of the eCall function provided by in‐vehicle nomadic devices. More precisely, experiments were conducted by more than 250 participants with different sociodemographic profiles in order to study the users' acceptance of the use of the developed eCall function for large‐scale usage. The collected data are analyzed, and results are reported. Performed experiments showed the usefulness, acceptance, and satisfactory performance of the eCall service. Copyright © 2016 John Wiley & Sons, Ltd.

RESEARCH EFFORTS ON INTELLIGENT TRANSPORTATION SYSTEM IN NIGERIA: DEVELOPMENT OF TRIP PLANNING MODELS

The critical situation of unwelcome frustration experienced by urban trip makers and roadside dwellers alike, calls for a very strong push by all stakeholders in the transportation sector to enhance the service performance of transportation facilities using Intelligent Transportation Systems (ITS). Needed strategies for successful ITS implementation in both developed and developing nations, include but not limited to policy formulation and planning, funding of ITS projects, development and deployment of research products, system performance measurement, optimization of the performance of existing infrastructure, capacity building, etc. which could be much promoted with research efforts. Advanced Public Transportation System (APTS) and Advanced Traveller Information System (ATIS) categories as products of research on ITS were developed with data derived from urban trip makers on captive corridors, both in Ilorin, Kwara State and Minna, Niger State, Nigeria. The fundamental basis and status of the research activities targeted at developing and deploying the ITS in Nigeria are highlighted in the paper. http://dx.doi.org/10.4314/njt.v35i3.4

Effect of density feedback on the two-route traffic scenario with bottleneck

In this paper, we investigate the effect of density feedback on the two-route scenario with a bottleneck. The simulation and theory analysis shows that there exist two critical vehicle entry probabilities [Formula: see text] and [Formula: see text]. When vehicle entry probability [Formula: see text], four different states, i.e. free flow state, transition state, maximum current state and congestion state are identified in the system, which correspond to three critical reference densities. However, in the interval [Formula: see text], the free flow and transition state disappear, and there is only congestion state when [Formula: see text]. According to the results, traffic control center can adjust the reference density so that the system is in maximum current state. In this case, the capacity of the traffic system reaches maximum so that drivers can make full use of the roads. We hope that the study results can provide good advice for alleviating traffic jam and be useful to traffic control center for designing advanced traveller information systems.

A Comparative Study of Three Multivariate Short-Term Freeway Traffic Flow Forecasting Methods With Missing Data

Short-term traffic flow forecasting is a critical function in advanced traffic management systems (ATMS) and advanced traveler information systems (ATIS). Accurate forecasting results are useful to indicate future traffic conditions and assist traffic managers in seeking solutions to congestion problems on urban freeways and surface streets. There is new research interest in short-term traffic flow forecasting due to recent developments in intelligent transportation systems (ITS) technologies. Previous research involves technologies in multiple areas, and a significant number of forecasting methods exist in the literature. However, most studies used univariate forecasting methods, and they have limited forecasting abilities when part of the data is missing or erroneous. While the historical average (HA) method is often applied to deal with this issue, the forecasting accuracy cannot be guaranteed. This article makes use of the spatial relationship of traffic flow at nearby locations and builds up two multivariate forecasting approaches: the vector autoregression (VAR) and the general regression neural network (GRNN) based forecasting models. Traffic data collected from U.S. Highway 290 in Houston, TX, were used to test the model performance. Comparison of performances of the three models (HA, VAR, and GRNN) in different missing ratios and forecasting time intervals indicates that the accuracy of the VAR model is more sensitive to the missing ratio, while on average the GRNN model gives more robust and accurate forecasting with missing data, particularly when the missing data ratio is high.