Advanced Traveler Information Systems Research Articles

Modeling Individual Travel Time with Back Propagation Neural Network Approach for Advanced Traveler Information Systems

AbstractThe heterogeneous driving behaviors from different travelers are not considered in current advanced traveler information systems (ATIS) such as Google Maps and 511 systems, which leads the ...

Short Term Traffic State Prediction via Hyperparameter Optimization Based Classifiers.

Short-term traffic state prediction has become an integral component of an advanced traveler information system (ATIS) in intelligent transportation systems (ITS). Accurate modeling and short-term traffic prediction are quite challenging due to its intricate characteristics, stochastic, and dynamic traffic processes. Existing works in this area follow different modeling approaches that are focused to fit speed, density, or the volume data. However, the accuracy of such modeling approaches has been frequently questioned, thereby traffic state prediction over the short-term from such methods inflicts an overfitting issue. We address this issue to accurately model short-term future traffic state prediction using state-of-the-art models via hyperparameter optimization. To do so, we focused on different machine learning classifiers such as local deep support vector machine (LD-SVM), decision jungles, multi-layers perceptron (MLP), and CN2 rule induction. Moreover, traffic states are evaluated using traffic attributes such as level of service (LOS) horizons and simple if–then rules at different time intervals. Our findings show that hyperparameter optimization via random sweep yielded superior results. The overall prediction performances obtained an average improvement by over 95%, such that the decision jungle and LD-SVM achieved an accuracy of 0.982 and 0.975, respectively. The experimental results show the robustness and superior performances of decision jungles (DJ) over other methods.

Routing in a Stochastic Network with Nonrecurrent Incidents: Behavioral Interpretation of Dynamic Traffic Assignment

AbstractIn an advanced traveler information system (ATIS), this study examines how to map a driver’s time constraints and risk-taking behavior to real-time routing in a probabilistic time-dependent...

Dynamic Origin-Destination Demand Estimation From Link Counts, Cellular Data And Travel Time Data

Accurate real-time Origin-Destination (O-D) demand estimation is critical for development of Advanced Traveller Information Systems (ATIS) as well as Advanced Traffic Management Systems (ATMS). Traditional O-D estimation techniques typically pose it as optimization problems involving offline calibration of O-D priors from historical data and updating these prior based on real-time data sources such as link counts, link travel times and Global Positioning System (GPS) probe data. However, two major drawbacks have emerged from this approach. Firstly, the generation of effective priors requires extensive population data with high temporal sampling frequency. Such data is generally hard to obtain and process with low memory and compute power. Secondly, the optimization problems proposed for are often non-convex in nature. Therefore, convergence to an optimal solution is not guaranteed. Moreover, the algorithms suggested don’t scale well with increase in the size of the transportation network. In order to tackle these challenges, we first generate priors through a convex optimization framework by Wu et al. (2015) for O-D estimation in quasi-static settings from cell phone Call Detail Records (CDRs). We extend this approach to a completely dynamic setting by utilizing real-time link counts and link travel times through an Entropy Maximization framework proposed by Janson et al. (1992). Approximate solution to the traffic assignment sub-problem is also achieved from Wu et al. (2015). We test this procedure for estimating commute demand for a simplified freeway network representing nine counties of the San Francisco Bay Area. We estimate demands for 2,916 O-D pairs for three hours at five-minute intervals, making it a 104,976 dimensional problem. We analyze the spatio-temporal distribution of errors, the effects of wider cell-phone coverage and updating estimates over time. Results indicate that an increase in cell-phone coverage from 0% to 100% leads to a reduction in average Root Mean Squared Error (RMSE) from 15.7 to 7.2 while updating estimates also leads to reductions in RMSE when O-D demand is high.

Travel Time Prediction under Mixed Traffic Conditions Using RFID and Bluetooth Sensors

Travel time information is an integral part in various ITS applications such as Advanced Traveler Information System, Advanced Traffic Management Systems etc. Travel time data can be collected manually or by using advanced sensors. In this study, suitability of Bluetooth and RFID (Radio Frequency Identifier) sensors for data collection under mixed traffic conditions as prevailing in India is explored. Reliability analysis was carried out using Cumulative Frequency Diagrams (CFDs) and buffer time index along with evaluation of penetration rate and match rate of RFID and Bluetooth sensors. Further, travel time of cars for a subsequent week was predicted using the travel time data obtained from RFID sensors for the present week as input in ARIMA modeling method. For predicting the travel time of different vehicle categories, relationships were framed between travel time of different vehicle categories and travel time of cars determined from RFID sensors. The stream travel time was then determined considering the travel time of all vehicle categories. The R-Square and MAPE values were used as performance measure for checking the accuracy of the developed models and were closer to one and lower respectively, indicating the suitability of the RFID sensors for travel time prediction under mixed traffic conditions. The developed estimation schemes can be used as part of travel time information applications in real time Intelligent Transportation System (ITS) implementations.

Grouped travel time estimation in signalized arterials using point-to-point detectors

Travel time, an essential parameter in Advanced Traffic Management Systems and Advanced Traveller Information Systems, has been studied for decades of years, especially on freeways. However, the traffic flows in signalized arterials are more complex and stochastic, which enables arterial travel times to be multistate due to the interruptions from signal controls. Therefore, the signal effects need to be considered in the estimation of arterial travel times. In this paper, a grouped travel time estimation method is proposed to consider the signal interruptions and estimate the arterial travel times on a more micro level. With the data obtained from point-to-point detectors, the method analyzes vehicles’ traffic states before signals and recognizes those vehicles sharing the same states in both upstream and downstream signals as vehicle groups. Then the estimation problem is formulated as a clustering process to recognize the vehicle groups, and the grouped travel times are represented by the centers of vehicle groups. The field tests under ten different scenarios show that the grouped travel times can capture the multistate properties of the travel times in signalized arterials and represent the observed travel times of most vehicles within minor deviations.

Active traffic management in urban areas: Is it effective for professional drivers? The case of variable message signs

Active traffic management is the ability to dynamically manage congestion based on prevailing and predicted traffic conditions. Several technologies and algorithms have been employed to develop a set of tools utilised by Traffic Control/Traffic Management Centres with the aim to mitigate disruptions and improve traffic flow in the road networks. One such tool, belonging to the Advanced Traveller Information Systems category is Variable Message Signs. Variable Message Signs are located in major roads and provide real-time traffic information to the drivers so as to modify their behaviour considering the prevailing traffic conditions. To this end, the question arises. Is active traffic management in urban road networks – as currently provided by Variable Message Signs – effective for professional drivers? In plain words, do professional drivers modify their route choice, i.e. divert under the provision of real-time traffic information? A stated preference survey was performed to truck and taxi drivers to identify their response to information provided by Variable Message Signs. Results, in the form of ordered probit models indicated that the provided information is not that relevant to the investigated driver categories, as it is for passenger car drivers.

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.

Path-Based Dynamic User Equilibrium Model with Applications to Strategic Transportation Planning

This study proposes an analytical capacity constrained dynamic traffic assignment (DTA) model along with an efficient path-based algorithm. The model can be applied to analyzing dynamic traffic demand management (TDM) strategies, but its specific feature is allowing for an evaluation of advanced traveler information systems (ATIS) within the strategic transportation planning framework. It is an extension of a former DTA model, where each link is given an infinite capacity and is assumed to be completely traversed inside any time interval it is reach by a path. Thereby, the length of time intervals should be very longer than the link travel times, and the link capacity constraints are overlooked. The paper rests on three key ideas to overcome these restrictions: (1) adding path-link fraction variables to the base model, allowing path flows to spread out over time intervals on long links; (2) uniformly dividing each link into smaller parts (segments), so that each part is more likely to be traversed inside a time interval; (3) imposing a dynamic penalty function on each link, thereby the capacity constraint can be included. The proposed DTA algorithm decomposes the augmented model in terms of origin-destination (OD) pairs and departure time intervals, and utilizes a dynamic column generation technique for generating active paths between the OD pairs. The optimal solution to a one-link network demonstrates that the model is able to approximate the dynamic flow propagation over a link with sensible accuracy. Besides, investigation of the results for a small test network reveals that the algorithm performs very well in computing temporal link flows and queuing delays. Finally, numerical experiments on a real life network indicate that the algorithm converges sufficiently fast and provides path information for each time interval. The network is further used to show the algorithm is capable of assessing a dynamic TDM strategy as well as an ATIS system.

Short-term prediction of lane-level traffic speeds: A fusion deep learning model

Accurate and robust short-term traffic prediction is an important part of advanced traveler information systems. With the development of intelligent navigation and autonomous driving, it is necessary to explore the lane-level predictions of traffic speeds. However, most existing traffic prediction models concentrate on forecasting the traffic flow characteristics of the entire road sections rather than those of certain lanes. This paper proposes a fusion deep learning (FDL) model to predict lane-level traffic speed. First, the entropy-based grey relation analysis is introduced to choose lane sections that are strongly correlated with the lane section to be predicted. Second, a two-layer deep learning framework is established by combining the long short-term memory (LSTM) neural network and the gated recurrent unit (GRU) neural network. Third, the ground-truth data of several lane sections captured by remote traffic microwave sensors (RTMS) on the 2nd Ring Road of Beijing are utilized to examine the FDL model and compare it with several benchmark models. The experimental result indicates that in addition to capturing the fluctuations of traffic speed at the lane level, the FDL model has better performance than the benchmark models in terms of prediction accuracy and stability.

A web service based advanced traveller information system for itinerary planning in an uncertain multimodal network

This article intends to provide an advanced traveller information system based on open geospatial consortium (OGC) standards to perform a fuzzy bi-criteria shortest-path analysis for a multimodal transportation network. To do so, chaining of OGC web services is used to resolve interoperability problems among distributed and heterogeneous resource data. First, a web feature service is assigned to each mode of transportation to gather and convert the data to a standard format, such as geographic markup language. Then, the obtained data are combined to model and construct the multimodal transportation network. Subsequently, a web processing service is designed for performing a fuzzy bi-criteria shortest-path analysis in the multimodal transportation network. Finally, the best routes between the origin–destination pair are presented on a digital map by using a web map service. In the study, among various route-selection criteria, travel time and comfort are considered. To model these criteria, the fuzzy set theory is utilized. By using fuzzy decision tables and a Tsukamoto fuzzy inference system, the desirability of the routes is calculated. The results of comparison among the four travel routes between various origin–destination pairs indicate that the proposed system could be an applicable tool for providing pre-trip information and assisting multimodal travellers under uncertain conditions.

TERP: Time-Event-Dependent Route Planning in Stochastic Multimodal Transportation Networks With Bike Sharing System

Advanced traveler information systems (ATISs) provide travelers with public transportation information to improve the quality of individual life and alleviate congestion as well as air pollution. However, existing works have not fully incorporated bike sharing systems within ATIS, providing no interaction with other modalities nor taking bike stocks into account. In addition, the uncertainty of traffic conditions and multimodal routing makes it challenging to accurately estimate the travel time. In this paper, we leverage large-scale historical data collected in London and construct a multimodal transportation network, including bus, tube, public bikes, and walking. We solve the modalities aggregation problem by practically modeling the travel time, arrival time, bike stock, and transfer time between different transport modalities. Furthermore, we propose TERP, a time-event-dependent route planner that optimizes both trip duration and reliability. We conduct experiments on extensive real-world data with over 23 million arrival records and 15 million stock records on more than 10 000 stations from transport for London platform (TfL). The results validates 14.91% reduction of actual total trip duration and 56.28% improvement in terms of route reliability in rush hours comparing with TfL.

Electromagnetic Wave Absorber Sheet for 940 MHz Dedicated Short Range Communication Frequency Bands Using Fe Based Alloy Soft Magnetic Metal Powder

The recent development of information and communication technologies brings new changes to automobile traffic systems. The most typical example is the advancement of dedicated short range communication(DSRC). DSRC mainly consists of an intelligent transportation system(ITS), an electronic toll collection system(ETCS) and an advanced traveler information system(ATIS). These wireless communications often cause unnecessary electromagnetic waves, and these electromagnetic waves, in turn, cause frequent system malfunction. To solve this problem, an absorber of electromagnetic waves is suggested. In this research, various materials, such as powdered metal and iron oxides, are used to test the possibility for an effective absorption of the unnecessary electromagnetic waves. The various metal powders are made into a thin sheet form by compositing through processing. The electromagnetic characteristics(complex permittivity, complex permeability) of the fabricated sheet are measured. As a result, we achieve –6.5 dB at 940 MHz(77.6 % absorption rate) with a 1.0 mm-thickness electromagnet wave absorber, and –9.5 dB at 940 MHz(88.8 % absorption rate) with a 2.0 mm-thickness absorber.

Exploring the use of advanced traffic information system to manage traffic congestion in developing countries

Abstract Presently, advanced traveler information system (ATIS) is extensively used elsewhere and is regarded as an important component for road traffic control and management. ATIS uses information and communication technology to broadcast traveler information to commuters to help them in scheduling their trips and also offer directions on the way. Information provided by ATIS may include: traffic condition (which may be presented in queue length, travel time, or delay), incidents such as road traffic crash and their geographical locations, events such as road construction and demonstrations, optimal routes, and inclement weather which may disrupt traffic. The traffic information may be transmitted via mechanisms such as radio and television, dynamic navigation systems (in-vehicle or mounted), internet services, smart phone services (e.g. specially designed application) and variable speed limit or changeable message signs systems. Providing commuters with accurate, timely and reliable traffic information will influence travel behavior and ultimately result in reduced travel time and traffic congestion along with reduced vehicle emissions and fuel consumption. Notwithstanding the benefits which can be derived from using intelligent transport systems to manage traffic, its use can be described as being in the basic stage in most developing countries. This paper discusses how ATIS can play a vital role of efforts to reduce traffic congestion in emerging nations.

Segment travel time route guidance strategy in advanced traveler information systems

In this paper, a segment travel time route guidance strategy was proposed in advanced traveler information systems. The route guidance information provided to the vehicles is the sum of travel time on equally divided segments. To evaluate its performance, the new strategy was compared with three other strategies in three different two-route scenarios based on the Nagel–Schreckenberg cellular automaton model. First, four strategies were applied to a symmetrical two-route scenario. The simulation results show that the smaller the segment length is, the greater the average flow will be. The more vehicles that choose roads based on the new strategy, the shorter the average travel time will be. There exists a optimal segment length for each the ratio of dynamic vehicles Sdyn which makes the travel time shortest. Second, in a symmetrical two-route scenario with a permanent bottleneck, results show that zero state and oscillation state were observed. When the bottleneck length more than 1000 cells, the oscillation state disappears. Finally, in a symmetrical two-route scenario with a temporary bottleneck, results show that when the inflow is high, the traffic network with Sdyn=0.5 is better than the one with Sdyn=0. When inflow is low, the situation is just opposite.

An extension of the technology acceptance model for understanding travelers' adoption of variable message signs.

Understanding travelers’ acceptance of Advanced Traveler Information Systems (ATIS) is crucial to the implementation of Intelligent Transportation Systems (ITS) capable of mitigating traffic congestion and improving network performance. This paper adopted an extended Technology Acceptance Model (TAM) to predict and explain road users’ intention to use Variable Message Sign (VMS) information. In addition to the traditional parsimonious TAM constructs (perceived usefulness, perceived ease of use and behavioral intention), the model examined the effects of attitude towards route diversion, familiarity with road network and information quality on road users’ acceptance of VMS. 762 drivers were interviewed and the obtained data were processed using Structural Equation Modeling. The results showed that travelers’ attitude towards route diversion had a positive effect on perceived usefulness and intention to use VMS. Information quality had a positive direct effect on perceived usefulness, perceived ease of use and attitude towards route diversion. Familiarity with the network had a positive effect on attitude towards route diversion and a negative effect on the perceived usefulness of VMS information. Perceived ease of use significantly and positively affected perceived usefulness and intention to use VMS. Perceived usefulness also had a positive effect on intention. Several academic and practical implications were also discussed.

A Bayesian Stochastic Kriging Optimization Model Dealing with Heteroscedastic Simulation Noise for Freeway Traffic Management

Since advanced traveler information and traffic management systems have become popular, it is vital to capture the joint impact of various strategies on transportation systems. Developing analytical models to incorporate traffic dynamics and travel behavior is challenging. Based on observation of the heteroscedasticity of the simulation noise in the stochastic simulator, this paper develops the Bayesian stochastic Kriging (BSK) model to adapt for the heteroscedastic noise and metamodel parameter uncertainty in a Bayesian framework, which enhances the existing surrogate-based optimization methods. This paper presents a metamodel for large scale simulation-based freeway traffic management optimization problems. Simulation-based optimization combines the advantages of simulation models and mathematical optimization methods. The parameter estimation of the BSK model is accomplished by the Bayesian inference. The proposed methodology enables the efficient use of large scale high-resolution traffic simulation models for simulation-based optimization, while accounting for travelers’ behavioral responses to information provision. We demonstrate the advantages of BSK compared to other existing metamodels using a numerical example of a synthetic network and a mathematical example. In a work zone scenario on a real-world freeway/arterial corridor of I-270 and MD-355 in the State of Maryland, the BSK model is applied to the freeway traffic management via optimizing the high-occupancy/toll rate and deploying dynamic message signs. Field traffic measurements by loop/microwave detections are used to calibrate travel demand and to supply the simulation parameters. The optimization results are promising in reducing the corridor-wide travel delay and enhancing the vehicle throughput. The online appendix is available at https://doi.org/10.1287/trsc.2018.0819 .

Empirical evaluation of real-time traffic information for in-vehicle navigation and the variable speed limit system

Advanced traveler information systems (ATIS) present traffic information to travelers to assist them in planning their journeys and also provide guidance when on the road. Providing travelers with information affects travel behavior and can result in improved performance and reduced congestion and emissions. However, the success of ATIS depends on, among other factors, whether drivers consider it useful and their particular responses to the provided information. There are, currently, many sources and market players in the traffic information industry producing traffic reports for consumers. The main objective of this paper was to examine whether traffic information provided from different sources are consistent with one other. A variable speed limit system and real-time traffic information system for in-vehicle navigation systems are used as a case study. This comparative analysis was made by superimposing the space-time regions of ground truth measured traffic conditions (“reality”) over the space-time regions of the reported information broadcasts. A predictive buffer was also introduced in the assessment. The predictive buffer was included to account for traffic information which could be considered as advance warning messages or correctly predicting and giving congestion information prior to the onset of actual congestion, which otherwise may have been reported as false alarms. This was motivated by the fact that optimal control approaches include the prediction of congestion in their control strategies. The methods developed in this research work have been applied to a real world problem to prove its applicability.

The influence of variable message signs on en-route diversion between a toll highway and a free competing alternative

In the field of road transport, Advanced Traveller Information Systems represent a relevant tool to manage road traffic, improve drivers’ utility and make a more efficient use of transport infrastructure. Due to the growing sources of en-route information available nowadays, it is crucial to understand better its influence on drivers’ behaviour, particularly with regard to route choice. Previous research in this field has mainly focused on the provision of en-route information in toll-free environments. However, few researches have explored its influence when a tolled alternative is available. This paper is aimed at exploring the influence of variable message signs (VMS) information on drivers’ route choice, made between a free highway and a competing tolled alternative. To that end, we develop a binary logit analysis based on empirical data from the metropolitan area of Madrid, Spain. Results show that the type of information provided to drivers through VMS panels significantly influences their route choice when one of the alternatives is tolled. Furthermore, some combinations of messages, such as adding travel time estimates together with incident messages, greatly increase the diversion rate to the tolled route. The research also offers evidence that the influence of the information provided changes according to the type of day, which may be related to traffic conditions, and to the different types of user characteristic of weekday and weekend mobility.

A two-stage policy for improving the efficiency of the traffic network by providing value-added ATIS service

Based on the pay-per-use charge in mobile Apps, value-added advanced traveler information systems (VATIS) service can be provided in traffic networks. From the perspective of the traffic manager, we propose a two- stage policy for improving the efficiency of traffic networks by providing VATIS service. At the first stage, the traffic manager selects several ATIS service providers, then offers seed fund to each of them to encourage VATIS service provision. At the second stage, the traffic manager announces a given target of the total market penetration of VATIS service and encourages service providers to achieve it together. If they could complete the task, compensation will be paid. To evaluate the efficiency of this policy, four scenarios are formulated by four bi-level models, and the total travel time is set as the evaluation indicator. Numerical experiments show that, the suggested policy can reduce total travel time dramatically if the market penetration target has been appropriately selected.