Traveler Information Systems Research Articles

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.

Vehicle Detection and Tracking for 511 Traffic Cameras With U-Shaped Dual Attention Inception Neural Networks and Spatial-Temporal Map

This paper develops vehicle detection and tracking method for 511 camera networks based on the spatial-temporal map (STMap) as an add-on toolbox for the traveler information system platform. The U-shaped dual attention inception (DAIU) deep-learning model was designed, given the similarities between the STMap vehicle detection task and the medical image segmentation task. The inception backbone takes full advantage of diverse sizes of filters and the flexible residual learning design. The channel attention module augmented the feature extraction for the bottom layer of the UNet. The modified gated attention scheme replaced the skip connection of the original UNet to reduce irrelevant features learned from earlier encoder layers. The designed model was tested on NJ511 traffic cameras for different scenarios covering rainy, snowy, low illumination, and signalized intersections from a key, strategic arterial in New Jersey. The DAIU Net has shown better performance than other mainstream neural networks based on segmentation model evaluation metrics. The proposed scanline vehicle detection was also compared with the state-of-the-art solution for infrastructure-based traffic movement counting solution and demonstrates superior capability. The code for the proposed DAIU model and reference models has been made public with the labeled STMap data to facilitate future research.

Predicting Airline Additional Services Consumption Willingness Based on High-Dimensional Incomplete Data

Prediction of the purchase willingness of passengers has great benefits for airlines to promote auxiliary services, however, the datasets stored in passenger travel information systems are often high-dimensional and incomplete. This study develops a prediction method of airline additional service consumption willingness based on high-dimensional and incomplete datasets with a triple-layer hybrid PSO-XGBoost model, which consists of an incomplete data processing layer, a high-dimensional data processing layer, and a predicting layer. The raw dataset is converted into a complete and low-dimensional dataset through the first two layers and inputted into the predicting layer to train and optimize the XGBoost model together with the PSO algorithm and 10-fold cross-validation. The experimental results show that the proposed method outperforms other traditional machine learning models, presenting the highest prediction score with 0.9879 in terms of AUC. The findings help predict airline additional services consumption intentions of passengers and are beneficial to efficient and low-cost precise marketing for airlines.

Delay Factors Modelling for Real-Time Traffic Information Systems

The paper is presenting an overview of the factors that lead to critical delays which may occur in multi-modal realtime traffic and travel information systems (MRTTI) employing cellular data network communications. The analysis is taking into account MRTTI systems that use mobile devices to detect position and speed of a vehicle and receive traffic information furthermore used to adjust a previously defined route from origin to destination points. Information transmission delays are also investigated for route guidance systems used in critical applications, such as emergency vehicles management. The identification of the delay factors and some models regarding the delays are presented along with measurements on a real system.

A new type of EU visa? The legal nature of a travel permit issued under the European Travel Information and Authorization System (ETIAS)

Głównym celem systemu ETIAS jest selekcja i monitorowanie osób przekraczających granicę zewnętrzną Unii Europejskiej poprzez wydawanie zezwoleń na wjazd. Autorzy tego nowego, zakrojonego na szeroką skalę projektu wyraźnie podkreślają, że zezwolenie ETIAS nie jest wizą, niemniej ten kolejny elektroniczny system autoryzacji wjazdu usprawni zarządzanie mobilnością obywateli 62 państw trzecich, a więc populacją 1,4 mld osób uprawnionych do bezwizowego przekraczania zewnętrznych granic UE. Niniejszy artykuł stanowi porównanie wiz Schengen oraz zezwoleń na podróż, które mają zostać wprowadzone na mocy rozporządzenia ETIAS w 2022 r. Analiza porównawcza zawiera szereg argumentów przemawiających za tezą, że zezwolenia wydawane w ramach nowego reżimu ETIAS wpisują się w szeroką definicję prawną wiz rozumianych jako upoważnienie do wjazdu na terytorium UE.

Machine learning for next‐generation intelligent transportation systems: A survey

AbstractIntelligent transportation systems, or ITS for short, includes a variety of services and applications such as road traffic management, traveler information systems, public transit system management, and autonomous vehicles, to name a few. ITS are expected to be an integral part of urban planning and future smart cities, contributing to improved road and traffic safety, transportation and transit efficiency, as well as to increased energy efficiency and reduced environmental pollution. On the other hand, ITS pose a variety of challenges due to its scalability and diverse quality‐of‐service needs, as well as the massive amounts of data it will generate. In this survey, we explore the use of machine learning (ML), which has recently gained significant traction, to enable ITS. We provide a thorough survey of the current state‐of‐the‐art of how ML technology has been applied to a broad range of ITS applications and services, such as cooperative driving and road hazard warning, and identify future directions for how ITS can further use and benefit from ML technology.

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.

A Hybrid Short-Term Traffic Flow Multistep Prediction Method Based on Variational Mode Decomposition and Long Short-Term Memory Model

Timely and accurate traffic prediction information is essential for advanced traffic management system (ATMS) and advanced traveler information system (ATIS). Because of the characteristics of nonlinearity, nonstationarity, and randomness, short-term traffic flow prediction could be still a challenging task. In this study, a hybrid short-term traffic flow multistep prediction method is proposed by combining the variational mode decomposition (VMD) algorithm and long short-term memory (LSTM) model. Firstly, the VMD algorithm is employed to decompose the original traffic flow data into a series of intrinsic mode function (IMF) components. Secondly, different LSTM models are established to predict different IMF components. For each prediction model, one-step to three-step predictions are carried out. Finally, the component prediction results are aggregated to obtain the final traffic flow multistep prediction values. The prediction performance of the proposed hybrid model is investigated using inductive loop data measured from the north-south viaduct expressway in Shanghai. The experiment results show that (1) VMD algorithm could effectively avoid the problems of endpoint effects and modal aliasing, and the decomposition effect is better than empirical mode decomposition algorithm and wavelet decomposition algorithm; (2) among all the involved methods, the proposed hybrid model is more effective and robust in extracting the trend information, which has the best multistep prediction performance.

The Application of Innovative Technology on Internal Border Control in Schengen Area

This article or writing aims to discuss the application of technology and certain mechanism as the impact of a disruptive era on internal border control in the Schengen area. The problem is focused on the internal border system which got reintroduced as a countermeasure against the 2015 Migrant Crisis in Europe. The analysis conducted in this research will measure whether the application of technology gives a practical benefit in alleviating internal border controls or negative impact such as discrimination towards refugees and immigrants in the Schengen area. In order to approach this problem a theoretical reference is used from international relations’ perspective, as a part of the political science greater scope on the issue. The data is collected through library research and analyzed qualitatively. This study concludes that various improvements and improvisation were made in the implementation of border controls. There are several steps taken by the EU for border control checks by utilizing technology such as Visa Information System (VIS), Schengen Information System (SIS), European Travel Information and Authorization System (ETIAS), and FRONTEX. These technologies allow Schengen states to share visa data about individual who enters European Union from its various borders, enable seamless cooperation regarding border controls and law enforcement and visitor tracking between countries. This study conclude that the application of innovative technology on internal border control in Schengen Area is well applied and projectively very useful to tackle the security concerns and conflicting interests within the Schengen area and remove the needs of unnecessarily long physical internal border controls.

Traffic Control Scheme for Social Optimum Traffic Assignment with Dynamic Route Pricing for Automated Vehicles

In transportation modeling, after defining a road network and its origin-destination (OD) matrix, the next important question is how to assign traffic among OD-pairs. Nowadays, advanced traveler information systems (ATIS) make it possible to realize the user equilibrium solution. Simultaneously, with the advent of the Cooperative Intelligent Transport Systems (C-ITS), it is possible to solve the traffic assignment problem in a system optimum way. As a potential traffic assignment method in the future transportation system for automated cars, the deterministic system optimum (DSO) is modeled and simulated to investigate the potential changes it may bring to the existing traditional traffic system. In this paper, stochastic user equilibrium (SUE) is used to simulate the conventional traffic assignment method. This work concluded that DSO has considerable advantages in reducing trip duration, time loss, waiting time, and departure delay under the same travel demand. What is more, the SUE traffic assignment has a more dispersed vehicle density distribution. Moreover, DSO traffic assignment helps the maximum vehicle density of each alternative path arrive almost simultaneously. Furthermore, DSO can significantly reduce or avoid the occurrence of excessive congestion.

Highway Travel Time Prediction of Segments Based on ANPR Data considering Traffic Diversion

Travel time is one of the most critical parameters in proactive traffic management and the deployment of advanced traveler information systems. This paper proposes a hybrid model named LSTM-CNN for predicting the travel time of highways by integrating the long short-term memory (LSTM) and the convolutional neural networks (CNNs) with the attention mechanism and the residual network. The highway is divided into multiple segments by considering the traffic diversion and the relative location of automatic number plate recognition (ANPR). There are four steps in this hybrid approach. First, the average travel time of each segment in each interval is calculated from ANPR and fed into LSTM in the form of a multidimensional array. Second, the attention mechanism is adopted to combine the hidden layer of LSTM with dynamic temporal weights. Third, the residual network is introduced to increase the network depth and overcome the vanishing gradient problem, which consists of three pairs of one-dimensional convolutional layers (Conv1D) and batch normalization (BatchNorm) with the rectified linear unit (ReLU) as the activation function. Finally, a series of Conv1D layers is connected to extract features further and reduce dimensionality. The proposed LSTM-CNN approach is tested on the three-month ANPR data of a real-world 39.25 km highway with four pairs of ANPR detectors of the uplink and downlink, Zhejiang, China. The experimental results indicate that LSTM-CNN learns spatial, temporal, and depth information better than the state-of-the-art traffic forecasting models, so LSTM-CNN can predict more accurate travel time. Moreover, LSTM-CNN outperforms the state-of-the-art methods in nonrecurrent prediction, multistep-ahead prediction, and long-term prediction. LSTM-CNN is a promising model with scalability and portability for highway traffic prediction and can be further extended to improve the performance of the advanced traffic management system (ATMS) and advanced traffic information system (ATIS).

What determines the acceptance of socially optimal traffic coordination?: A scenario-based examination in Germany

Social optimization emerged as a strategy for the attainment of more efficient road traffic (i.e., minimal total or average travel time). In order to achieve efficiency benefits, collective action is needed. A major challenge is that not every individual benefits equally from social optimization. Thus, a corresponding technological implementation (i.e., a social optimizing traveler information system (SOTIS)) is prone to rejection. The question remains as to which factors underlie the acceptance of such a system. Therefore a sample of 391 car drivers in Germany, stratified by kilometers driven per year, age and gender, completed a scenario-based questionnaire addressing attitudinal acceptance of a SOTIS. The results indicate that participants reacted rather hesitantly towards incorporating a SOTIS into daily driving. Indeed, structural equation modeling revealed that the behavioral intention to use it was promoted by its perceived usefulness. However, this intention was negatively affected by elicited subjective ambivalence (i.e., simultaneous conducive and opposing affect towards the SOTIS). Opposition in subjective ambivalence could be explained by perceived injustice. Social factors (i.e., a social norm and image gains due to using the system) were positively associated with perceived usefulness. The high social dependency and the effect for perceived injustice were not replicated in a second sample of 111 drivers that evaluated a user-optimizing traveler information system (UOTIS). The results suggest to taking active ambivalence reduction through transparent prevention of injustice, and the promotion of a conducive social norm into account in the planning and design of a SOTIS. Additionally, this study provides an empirically tested framework for examining the emergence and maintenance of acceptance of such a system.

The role of coordination costs in mode choice decisions: A case study of German cities

Abstract In times of accelerating urbanization and environmental pollution, mode choice decisions (MCD) are a critical parameter in a city’s appearance and its environmental impacts. Simultaneously, the emerging smartphone multimodal traveller information systems (SMTIS) simplifies the usage of multimodal trips and, therefore, enhance the options in MCD. Current MCD models, in addition to considering classic parameters like travel time and cost, also consider socioeconomic variables and latent variables, such as modal preferences or mode-specific characteristics. However, from the users’ perspective, one main influence is currently still not sufficiently considered in these models: Coordination costs for planning the trip, such as looking-up time tables for public transport. Consequently, we introduced this variable in a multinomial logit model and made a representative survey in Germany for measuring the coordination effort and evaluating our model. Our results support our hypothesis that coordination costs have a significant impact on MCD. We therefore conclude that further developments in information systems together with supporting policies may influence the MCD and, hence, lead to more sustainable cities in the future.

Smart Region Mobility Framework

A smart city describes an urban setting which aims to effectively apply ICT technologies to help improve the well-being of its citizens and reduce the negative impacts of urbanization. The priority areas considered in the Global Smart City Index (SCI) by the Institute for Management Development’s (IMD) World Competitiveness Centre were key infrastructures and technologies in (1) health and safety, (2) mobility, (3) activities (e.g., recreational spaces), (4) opportunities (work and school), and (5) governance. A smart region is a term used to extend the concept of a smart city into both urban and rural settings to promote a sustainable planning approach at the regional level. A direction that must be considered is the adoption of a “Smart Region Mobility Framework” to effectively transform our urban and rural regional transportation networks. This research study focused on the development of the smart region mobility framework for an island region group in the Philippines. The smart region goal is to integrate intelligent transportation system (ITS) platforms such as advanced public transportation system (APTS), advanced traveler information system (ATIS), and advanced rural transportation system (ARTS) to the local public transportation route plans (LPTRP) of the region. The activities include the data collection, analysis, and evaluation of multimodal regional transportation networks and social services infrastructure. The transportation network modeling process follows the four-step transportation planning process of trip generation, trip distribution, modal-split analysis, and trip assignment. Based on the analysis of 6 provinces, 16 cities, and 114 municipalities included in the study, there are two cities identified as smart city candidates. One of the smart city candidates is designated as the smart city regional center. In the context of a smart region, the available social services (e.g., employment opportunities, education, and health services) in the designated smart cities can also be made accessible to connected cities/municipalities through ease of transportation and mobility services in the region. Lastly, the study presented the implementation of data flow architecture of the smart region mobility framework, and the regional traveler information system using mobile and web application services.

Multitask Learning with Knowledge Base for Joint Intent Detection and Slot Filling

Intent detection and slot filling are important modules in task-oriented dialog systems. In order to make full use of the relationship between different modules and resource sharing, solving the problem of a lack of semantics, this paper proposes a multitasking learning intent-detection system, based on the knowledge-base and slot-filling joint model. The approach has been used to share information and rich external utility between intent and slot modules in a three-part process. First, this model obtains shared parameters and features between the two modules based on long short-term memory and convolutional neural networks. Second, a knowledge base is introduced into the model to improve its performance. Finally, a weighted-loss function is built to optimize the joint model. Experimental results demonstrate that our model achieves better performance compared with state-of-the-art algorithms on a benchmark Airline Travel Information System (ATIS) dataset and the Snips dataset. Our joint model achieves state-of-the-art results on the benchmark ATIS dataset with a 1.33% intent-detection accuracy improvement, a 0.94% slot filling F value improvement, and with 0.19% and 0.31% improvements respectively on the Snips dataset.

The Association between ICT-Based Mobility Services and Sustainable Mobility Behaviors of New Yorkers

The energy consumption and emissions in the urban transportation are influenced not only by technical efficiency in the mobility operations but also by the citizens’ mobility behaviors including mode choices and modal shift among sustainable and unsustainable mobility modes. Information and Communication Technologies (ICTs) can play an important role in the mobility behaviors of citizens, and it is necessary to study whether ICTs support sustainable mode choices like public transport and nonmotorized modes, which increase the total energy efficiency in the urban mobility and reduce traffic congestion and related emissions. This paper focuses on the two most popular ICT services in the urban transport, which are ATIS (Advanced Traveler Information Systems), and ridesourcing services. This study used the New York Citywide Mobility Survey (CMS) findings with a sample of 3346 participants. The associations between using these two ICT services and the mobility behaviors (mode choice with ATIS and modal shift to ridesourcing) are analyzed through a multinomial logistic regression and descriptive statistics, and the results are compared with similar international studies. The findings indicate that the respondents who use ATIS apps more frequently are more likely to use rail modes, bicycles, bus/shuttles, and rental/car sharing than private cars for their work trips. Moreover, the findings of the modal shift to ridesourcing indicate that the most replaced mobility modes by ridesourcing services are public transport (including rail modes and buses), taxis, and private cars, respectively.

How does interchange affect passengers’ route choices in urban rail transit? – a case study of the Shanghai Metro

ABSTRACT Interchange provides more flexibility in route choice, a key travel behaviour in urban rail transit, but its influence is usually simplified. This paper investigates how interchange affects route choice with passenger perception considered. At single-interchange level, perceived interchange time was proposed and modelled under three resolutions to capture passenger perception and its sensitivity. At route level, the influence of interchange was modeled by first comparing eight quantifications of interchange. Mixed logit models with the best interchange proxy were further developed to address the correlation among alternative routes and reveal the potential taste variations among passengers. Results based on Shanghai Metro data showed perceived interchange time, including passenger perception and interchange environment, better represents the influence of interchange in route choice, meanwhile the weights of interchanges on one route rise sequentially and non-linearly. The results can improve route choice prediction in demand modelling and route recommendation in advanced traveller information systems.

Travel Route Support Service System Using Information of Elderly Based on IoT Environment

Background/Objectives: Interest in the industry aimed for the aged continues to grow due to an increase in the elderly population. Due to the increase in the elderly population with economic consumption capacity, demand for the welfare, medical care and travel industry aimed for the aged is expanding. General travel information and programs are provided mainly by travel service providers. The elderly-centered travel information service has many shortcomings. Methods/Statistical Analysis: The Fourth Industrial Revolution can provide new fusion and convergence services through the fusion of existing industries and IT. Various services such as medical care, welfare and tourism are required in connection with the industry aimed for the aged. This paper proposes a service system supporting travel routes using IoT environment-based elderly information. As the IoT environment and IoT device distribution become more common, the smart device accessibility for the elderly has improved. The proposed system provides travel route support services suitable for the elderly by additional support for elderly based on existing travel information providing systems. Findings: The travel information system provides users with only general information, such as information about destinations or distance between destinations. The service system supporting travel routes using IoT environment-based elderly information proposed in this paper provides a list of route support services suitable for the elderly by additionally applying the information of the elderly, when the elderly request travel routes. Improvements/Applications: The service system supporting travel routes using IoT environment-based elderly information provides user-centered travel route support service from provider-centered service for travel information.

A Comprehensive Model to Study the Dynamic Accessibility of the Park & Ride System

A Park and Ride (P&R) system is an intermodal point where private vehicle users transfer to public transport to make a combined trip. Several researchers have measured the static accessibility of the P&R system. However, studying dynamic accessibility leads to a comprehensive model consisting of a series of elements and steps that allow for travel time analysis in various traffic conditions. Therefore, the purpose of this article is to develop an integrated model that provides a set of procedures which determine the travel origin points of the P&R system, calculate the trips in different traffic conditions (in this article, the results are shown in three scenarios: no traffic (NT), low traffic (LT), and heavy traffic (HT)), and include a hot spot analysis and correlation in order to finally be able to display the dynamic accessibility using geospatial software. The result shows that the accessibility of the P&R system varies depending on the traffic volume and the variation of the accessibility in the different areas that conform to the urban environment. In conclusion, the integrated model helps users decide on the best time to travel to the P&R system, allows transport planners to develop strategies to make the system more functional, and gives an excellent opportunity to develop a travel information system.