Advanced Traveler Information Systems Research Articles

Reducing strategic uncertainty in transportation networks by personalized routing advice: A route-choice laboratory experiment

One fundamental reason causing traffic flow instability and the difficulties in predicting traffic conditions is strategic uncertainty. This paper experimentally investigated how routing advice influenced strategic uncertainty and analyzed compliance behavior and decision time that might affect strategic uncertainty. Subjects participated in a route-choice game in which they were required to choose among three routes from one origin to one destination. Two routing advice strategies, user-optimal recommendation (UOR) and system-optimal recommendation (SOR), taking into account subjects’ choice inertia, were investigated. The experimental results indicated that (i) UOR could reduce strategic uncertainty and stabilize traffic flow, whereas SOR was counterproductive, (ii) subjects were more likely to comply with UOR compared with SOR and preferred to follow the advice with the same choices as the previous round rather than the route switching advice, (iii) the subjects with faster decisions (intuition) had higher compliance rates and lower travel costs than those with slower decisions (deliberation). Our experimental findings suggest that fairness and psychological factors are crucial in route choice, providing valuable guidance in designing personalized advanced traveler information systems.

Dynamic Lane Reversal Strategy in Intelligent Transportation Systems in Smart Cities.

Route guidance strategies are an important part of advanced traveler information systems, which are a subsystem of intelligent transportation systems (ITSs). In previous research, many scholars have proposed a variety of route guidance strategies to guide vehicles in order to relieve traffic congestion, but few scholars have considered a strategy to control transportation infrastructure. In this paper, to cope with tidal traffic, we propose a dynamic lane reversal strategy (DLRS) based on the density of congestion clusters over the total road region. When the density reaches 0.37, the reversible lane converts to the opposite direction. When the density falls off to below 0.22, the reversible lane returns back to the conventional direction. The simulation results show that the DLRS has better adaptability for coping with the fluctuation in tidal traffic.

Data collection and analysis applied to intelligent transportation systems: a case study on public transportation

The big data concept has been gaining strength over the last few years. With the arise and dissemination of social media and high access easiness to information through applications, there is a necessity for all kinds of service providers to collect and analyze data, improving the quality of their services and products. In this regard, the relevance and coverage of this niche of study are notorious. It is not a coincidence that governments, supported by companies and startups, are investing in platforms to collect and analyze data, aiming at the better efficiency of the services provided to the citizens. Considering the aforementioned aspects, this work makes contextualization of the Big Data and ITS (Intelligent Transportation System) concepts by gathering recently published articles, from 2017 to 2021, considering a survey and case studies to demonstrate the importance of those themes in current days. Within the scope of big data applied to ITS, this study proposes a database for public transportation in the city of Campinas (Brazil), enabling its improvement according to the population demands. Finally, this study tries to present clearly and objectively the methodology employed with the maximum number of characteristics, applying statistical analyses (box-and-whisker diagrams and Pearson correlation), highlighting the limitations, and expanding the studied concepts to describe the application of an Advanced Traveler Information System (ATIS), a branch of Intelligent Transportation System (ITS), in a real situation. Therefore, besides the survey of the applied concepts, this work develops a specific case study, highlighting the identified deficiencies and proposing solutions. Future works are also contemplated to expand this study and improve the accuracy of the achieved results.

Pedestrian reported activity and information preference while waiting at a red light

AbstractThe development of pedestrian signal lights made it a promising information center to distribute information to assist safe road crossing and provide “controlled engagement” to improve waiting experience. To determine what types of information to display, this study surveyed 555 pedestrians on frequency of their existing activities while waiting at a red light and preference of several types of information based on results of a structured interview. Pedestrian reported 14 types of activities, from which the four categories with decreasing frequency were extracted based on factor analysis: scene perception, recreation, mental activity, and anxiety activity. The participants reported more mental and anxiety activity have higher need for external displayed information on signal lights. Overall, among the four information categories extracted from 24 specific information types with factor analysis, pedestrians preferred road crossing and contextual information the most, with less preference on learning and appreciation information. The least preferred information is entertainment information, within which they even disliked entertainment news and general commercial ads ( vs. local ones). There are also individual differences in preference for these information categories. The findings have implications on information design for the roadside signal lights as well as advanced traveller information system in general.

Modeling the Combined Effect of Travelers’ Contrarian Behavior, Learning and Inertia on the Day-to-Day Dynamics of Route Choice

Understanding the many facets of repeated route choice behavior in traffic networks is essential for obtaining accurate flow forecasts and enhancing the effectiveness of traffic management measures. This paper presents a model of the day-to-day evolution of route choices incorporating travelers’ contrarian behavior, learning and inertia. The model is formulated as a discrete-time nonlinear dynamical system, and its properties are investigated analytically and numerically with a focus on the effect of the fraction of individuals adopting a contrarian route choice behavior. The findings of the study indicate that the extent of contrarian behavior may have significant impacts on the attractiveness and stability of network equilibria as well as on global system performance. We show that a properly balanced combination of direct and contrarian subjects can protect the system from instabilities triggered by other behavioral and network features. Our results also suggest that the fixed point stability range may depend to a considerable extent on travelers’ inertia and memory of previous experiences, as well as on the form of the travel cost functions used in the model. The occurrence of contrarian behavior should be explicitly taken into account in the design of traffic management schemes involving the deployment of Advanced Traveler Information Systems (ATISs), as it may act as a mitigating factor against the concentration of choices on the recommended routes. The analytical framework proposed in this paper represents a novel contribution, since contrarian behavior in repeated route choice has been investigated mainly by means of empirical or simulation approaches thus far.

Modeling and Analysis of Driving Behaviour for Heterogeneous Traffic Flow Considering Market Penetration under Capacity Constraints

Based on analytical and simulation methods, this paper discusses the path choice behavior of mixed traffic flow with autonomous vehicles, advanced traveler information systems (ATIS) vehicles and ordinary vehicles, aiming to promote the development of autonomous vehicles. Firstly, a bi-level programming model of mixed traffic flow assignments constrained by link capacity is established to minimize travel time. Subsequently, the algorithm based on the incremental allocation method and method of successive averages is proposed to solve the model. Through a numerical example, the road network capacity under different modes is obtained, the impact of market penetration on travel time is analyzed, and the state and characteristics of single equilibrium flow and mixed equilibrium flow are explored. Analysis results show that the road network can be maximized based on saving travel time when all vehicles are autonomous, especially when the autonomous lane is adopted. The travel time can be shortened by increasing the market penetration of autonomous vehicles and ATIS vehicles, while the former is more effective. However, the popularization of autonomous vehicles cannot be realized in the short term; the market penetration of autonomous vehicles and ATIS vehicles can be set to 0.2 and 0.6, respectively, during the introduction period.

An empirical investigation of driver car-following risk evolution using naturistic driving data and random parameters multinomial logit model with heterogeneity in means and variances

This study aims to address the questions of how driving risk evolves during car-following processes and what factors contribute to the underlying evolution patterns. An empirical study is conducted using real world car-following data collected in the Shanghai Naturalistic Driving Study (SH-NDS). The evolution of the driving risk induced by the dynamic coupling between the leading and following vehicles during the car-following process is characterized by how an instantaneous crash-risk measure - rear crash risk index (RCRI) - changes by time. A spectral clustering analysis is first conducted to classify the driving risk evolution of the observed car-following maneuvers, showing the existence of five distinctive risk evolution patterns in the car-following processes. In order to investigate the relationship between the identified driving risk evolution clusters and their contributing factors, a regression analysis employing a random parameter multinomial logit model with heterogeneity in means and variances is followed, revealing several significant contributing factors to the car-following risk evolution patterns, such as congestion level, driver’s ability to maintain stable headways, and vehicle deceleration. This study has provided important insights into driving risk from the new perspective of risk evolution patterns, which is expected to have significant implications for the future development of advanced traffic management and traveler information systems (ATMS/ATIS) strategies, advanced driver assistance systems (ADAS), and connected and autonomous vehicles (CAV).

Estimation of Bus Arrival Times Using APC Data

Bus transit operations are influenced by stochastic variations in a number of factors (e.g., traffic congestion, ridership, intersection delays, and weather conditions) that can force buses to deviate from their predetermined schedule and headway, resulting in deterioration of service and the lengthening of passenger waiting times for buses. Providing passengers with accurate bus arrival information through Advanced Traveler Information Systems can assist passengers’ decision-making (e.g., postpone departure time from home) and reduce average waiting time. This article develops a set of regression models that estimate arrival times for buses traveling between two points along a route. The data applied for developing the proposed model were collected by Automatic Passenger Counters installed on buses operated by a transit agency in the northeast region of the United States. The results obtained are promising, and indicate that the developed models could be used to estimate bus arrival times under various conditions.

Deep reinforcement learning for dynamic incident-responsive traffic information dissemination

This study is concerned with the optimal dynamical information dissemination (DID) problem in a transportation network interrupted by traffic incidents. Optimizing system performance with DID after road incidents is challenging because of the uncertainty in traffic flow variation and travelers’ heterogeneous responses to information. To address the problem, we consider a traffic manager who aims to improve the system performance by dynamically generating and disseminating information to road users in a time period after an incident happens. We develop a decision tool for obtaining DID strategy based on double deep Q-learning (DDQL) for the traffic manager, aiming at finding an optimal DID strategy. The decision tool is integrated with traffic sensors which collect traffic data in real time. With advanced traveler information systems, the DID system dynamically sends out various types of information to users according to the current and anticipated traffic states so as to minimize congestion and enhance road network capacity. In particular, the proposed DDQL method utilizes a double deep Q-network (DQN) structure to learn the state–action values. To test and evaluate the performance of the decision tool, we develop a microscopic simulation model of a real road network in the Serangoon area of Singapore in PTV VISSIM and calibrate the model with real historical traffic data. We train and compare the DDQL controller model with different reward signals, including the weighted sum of the average speed and queue delay, total traffic flow, and average travel time. Numerical experiments demonstrate the good performance of the proposed DDQL-based DID approach in improving the congestion and other performance metrics of the expressway. The robustness and generalizability of the DDQL agent are also verified by evaluating the algorithm performance in environments with different traffic demand patterns and driving behavior profiles.

Competition and coordination in public transport: A mode choice experiment

Public transport plays an important role in sustainable transportation by reducing traffic congestion and greenhouse gas emissions. This study investigated the competition and coordination behavior in public transport mode choice with both positive and negative externalities. Subjects participated in a mode-choice game in which they were required to choose between bus and metro. The externalities in one public transport mode were determined by its characteristics and how many individuals chose it. We examined two treatments with different amounts of information: less information and more information, corresponding to non-implementing and implementing advanced traveler information systems, respectively. We derived three different theoretical solutions (i.e., the pure-strategy Nash equilibrium, the mixed-strategy equilibrium, and the fair-reference point) as benchmarks to compare with the experimental outcomes, finding that the fair-reference point was achieved when more information was provided, whereas the pure-strategy Nash equilibrium was better to predict the choice behavior when providing less information. Furthermore, the comparison of the two treatments revealed the information paradox, indicating that information affected the public transport mode choice behavior, and more information might lead to worse outcomes. The spontaneous collective actions induced by less information and risk aversion could exert the benefits from positive externalities, thereby reducing travel costs compared with more information. Also, we adopted an adaptive learning model to reproduce the main findings in the experiment and exhibit simulated results under different levels of information provision, providing a parsimonious explanation for the information paradox.

Study on the Route-Selection Behavior of Bus Passengers Based on an Evolutionary Game under the Condition of Information Guidance

Bus guidance information supplied by advanced traveler information systems provide a reference for route selection by bus passengers. This study analyzes the interaction between bus passengers’ route-choice behavior and the relationship in a game. The game revenue of the passengers waiting on the bus platform is quantified by parameters such as the number of passengers waiting on the platform, the number of passengers on the bus, and the passenger capacity of the bus. Based on the characteristics of passenger-bounded rationality, an evolutionary game model of bus choice behavior under information-induced conditions is established. Through the evolutionary game model, the distribution of the proportion of passengers receiving the guidance strategy is obtained when the evolution is stable. Finally, according to the distribution results, the passenger guidance strategy is discussed to achieve a win-win situation between passengers and bus operators.

Interactive route choice and prescriptive information on degradable network

This study models and analyzes the dynamic interaction between route choice and prescriptive information on a degradable network. The predictively prescriptive information is first explicitly modeled based on travelers’ behavior and historical data. Its accuracy is then dynamically measured to adjust the information compliance rate based on travelers’ expectations and experiences. Finally, a logit function is adopted to describe travelers’ route choice. Based on the proposed model, the dynamic interaction between route choice and prescriptive information on a degradable network is investigated by theoretical analyses and numerical experiments, which provides insights for the design and operation of advanced traveler information systems and traffic management.

Day-to-day dynamics in a simple traffic network with mixed direct and contrarian route choice behaviors

Contrarian behavior is an attitude leading individuals to act “contrary to the crowd” and can be observed in a variety of collective systems. The aim of this study is to investigate the effects of such behavioral trait on the day-to-day evolution of a traffic system in a simple two-route scenario. To this end, we formulate a nonlinear, discrete-time dynamical system and analyze how the fraction of contrarian-like choices affects fixed point uniqueness and stability, as well as mean travel cost evaluated both for the network as a whole and separately for subjects adopting either type of route choice behavior. We show that uniqueness and stability of the fixed point of the dynamical system are ensured inside certain ranges of the fraction of contrarian subjects within the traveling population, and that the width of such ranges depends on the sensitivity of actual costs to flows and on the level of accuracy of individuals’ cost perception. Moreover, instability is seen to take different forms (oscillatory behavior or convergence to alternate fixed points) depending on which limit of the stability region is crossed by the fraction of contrarians. Results of the analysis also indicate that introducing memory and learning in the process of expected cost formation tends to counter the destabilizing effect of strongly flow-dependent costs and highly cost-sensitive route choices. Finally, direct and contrarian travelers attain the same mean travel cost throughout the fixed point stability region, while a competitive advantage of the minority group emerges outside such region, albeit at the price of a deteriorated global network performance. Overall, the findings of the study support the conclusion that a well-balanced diversification of the traveling population in terms of direct and contrarian route choice attitudes has the potential to protect the system against instabilities induced by other behavioral and network features, and that this beneficial effect can be amplified by the occurrence of learning in the formation of perceived travel costs. It is suggested that contrarian behavior may contribute to mitigating the adverse effects of concentration of choices on the “best” routes and overreaction to the supplied information, induced by Advanced Traveler Information Systems, and thus should be explicitly considered in the design of traffic management strategies involving the deployment of such systems.

Delay or travel time information? The impact of advanced traveler information systems on drivers’ behavior before freeway work zones

Peak travel times contribute to congestion formation at freeway work zones. Advanced Traveler Information Systems (ATIS) can inform drivers in real-time about the delays and travel times en-route and can provide information about an alternative route to a destination. Different ATIS display strategies are available; however, road authorities lack insights into how time display methods and sign characteristics influence the driving behavior (decelerations, lateral position), the drivers’ attention allocation ability and the subsequent route choice before the freeway diversion. A driving simulator study was conducted with 80 drivers in the State of Qatar to investigate drivers' behavior and voluntary route choices when encountering total travel time (default setting) or delay time updates for two freeway routes on Variable Message Signs (VMS) and Graphical Route Information Panels (GRIP). The GRIPs are a graphical alternative to conventional VMS that can provide drivers with a visual map of the most direct route or an alternative less congested route to a destination using different color schemes. The time difference ratio between the two routes was kept constant to compare the effectiveness of the information designs and investigate the drivers’ attention towards the signs with an eye-tracker. The results showed that the display of zero delays for a detour did influence 74–83% of the drivers to take the alternative route when being displayed on a VMS and a GRIP with free flow attribute framing. When displaying equal total travel times, the GRIP did influence 25% more drivers to follow the alternative route than the VMS. Generally, displaying zero delays for the alternative route resulted in an efficient attention allocation to the first ATIS location and fewer mean decelerations before the repeated ATIS location nearing the diversion. Road authorities are advised to activate the display of delay times to support efficient route choices among freeway drivers.

Investigating traveller’s overall information needs: What, when and how much is required by urban residents

Advanced Traveller Information Systems (ATIS) play a critical role in travel planning; however, relatively little is known about people’s overall information needs in urban areas. This study aims to investigate urban traveller’s information demand as to the quantities, categories and levels as well as gain a deeper understanding of the drivers of demand for information. A two-stage approach, combining exploratory factor analysis with ordered probit is implemented to analyse multi-dimensional and inter-related information alternatives. An empirical analysis was conducted using a survey of 253 face-to-face interviews, collected in 2015 in Chengdu, China. The results indicate that demand is generally higher for public transport/road usage and real-time information. Traveller’s information needs also vary significantly across types, timing of acquisition and population segments. The findings suggest that merging and providing high quality information relating to travel time saving, knowledge gaining and uncertainty reducing in a single digital app is likely to deliver greater commercial opportunities to information/mobility service providers. The implications for policy and travel demand management are also provided.

Day-to-day traffic user equilibrium model considering influence of intelligent highways and advanced traveler information systems

Day-to-day traffic user equilibrium model considering influence of intelligent highways and advanced traveler information systems

Modelling travellers’ behaviour in a route choice experiment with information under uncertainty: calibration, validation, and further refinements

ABSTRACT Several systems have been proposed in recent years for Advanced Traveler Information Systems (ATIS), ranging from personal (mobile application, on-board) navigation to VMS (Variable Message Signs). All these applications are essentially aimed at assisting the travelers in their travel choices by means of technologically dispatched information about traffic network structure and traffic conditions. It is well known that in ATIS applications one of the key issues to be addressed concerns the accuracy of the dispatched information, here intended as the ability of the system in supplying information that can be verified by the travelers to be correct (affected by a small error). This paper aims at proposing some analytical models able to simulate the effects of the (in)accuracy on travelers’ compliance with information.

A GATs-GAN framework for road traffic states forecasting

Short-term traffic states forecasting of road networks based on real-time data is an important component of intelligent transportation systems, especially advanced traffic management systems and traveller information systems. By considering the influence of both space and time dimensions, we proposed a novel GATs-GAN framework for the forecasting of traffic states. First, to capture spatial traffic relationships, the traffic topological graph network is set up based on the connection of traffic sections. Then, the first-order neighbours and high-order neighbours of traffic networks can be structured. Graph attention networks (GATs) are used to obtain the hidden features of input traffic data by training the attention between nodes in high-order neighbours. Based on two traffic networks in California and Seattle in the United States, we find that the GATs-GAN with high-order neighbours can satisfactorily estimate the traffic data and performs better than the baseline methods and comparative experiments.

Road Travel Time Prediction Based on Improved Graph Convolutional Network

Travel time prediction is playing an increasingly important part in advanced traveler information system (ATIS), which is of great significance to alleviate urban traffic congestion. Although graph convolutional networks have been widely used in road network traffic prediction, spatiotemporal dynamic modeling of urban traffic is still an intractable task. In this study, we propose an improved graph convolutional network (IGC-Net) for travel time prediction. Specifically, we design a modified adjacency matrix by fusing distance and correlation matrix with original adjacency matrix to capture spatial dynamic feature. We then establish three components based on temporal property to capture recent, daily-periodic, and weekly periodic correlations. The comparison experiments with baseline models and variants on a real-world dataset in Beijing are conducted. The results show that the IGC-Net outperforms baseline models in different prediction horizons and has stronger robustness for dynamic traffic prediction.

A Framework for Dynamic Advanced Traveler Information Systems

This paper presents the framework for a dynamic Advanced Traveler Information System (ATIS). The ATIS currently in use provides users with stereotyped travel options, but the set of available modes in a given place and time is not the same for each traveler, and such a personal choice set varies within the context of daily trip chains. The research presented in this paper addressed these limitations by including dynamic features in the proposed system. The activity chain that the user performs as well as the personal mode availabilities are modelled simultaneously to define the logical architecture of an innovative information system. Such a technology was intended to assist travelers in performing their daily trip chaining. In order to provide some insight regarding the efficacy of the proposed procedure, a pilot test was performed using real travel time information. Results have shown that the ATIS proposed in this study might generate a significant reduction in travel times.