Traveler Information Systems Research Articles

An overview of key emerging technologies transforming public transportation in the Fourth Industrial Revolution era

Public transportation connects a large proportion of the world’s population to their destination. Hence it is vital to continuously monitor and improve the quality of services offered in line with technological advances in order to enhance travellers’ experience. This study aims to assess the technologies within the fourth industrial revolution that can be used to improve travellers’ experience of public transportation systems. The study followed a systematic review and extant literature was carefully reviewed to properly understand how new-age technologies can be used to improve travel experience in the public transportation sector. The study revealed that there are five key emerging technologies that can be used to transform the public transportation system. These technologies include operations management systems, traveller information systems, e-fare collection systems, intelligent vehicle and e-security systems. In conclusion, incorporating these emerging technologies with the existing public transportation system will drastically increase operational efficiency and improve travelling experience for users.

New methodologies for predicting corridor travel time mean and reliability

ABSTRACT Accurate travel time prediction is very important for real-time traveller information systems. Many existing traveller information systems provide point estimates of forecast travel times. Often the forecast corridor travel time is estimated as a direct summation of the forecast link travel times on the route. This approach neglects the correlation between link travel times and may lead to inaccurate route travel time forecasts. This paper improves upon the simple addition method by accounting for the dependency of link travel times on the arrival time at that specific link which further correlates to its preceding links. In addition, this paper also explores the potential of using the nonlinear autoregressive with exogenous inputs (NARX) model and feedforward neural network model to forecast the corridor travel time mean and reliability metrics. To the authors knowledge this is the first time, short-term travel time reliability is measured by a reliability interval which is based on the forecasts of corridor travel time mean and standard deviation. The prediction methodologies developed in this paper are tested on an urban arterial that has been instrumented with Bluetooth readers so empirical travel times are available. It was found that the proposed NARX model outperforms the other models that were studied with respect to mean corridor travel time prediction. In terms of the reliability interval prediction, the performance of various models is presented as a Pareto Optimal Frontier trading off accuracy and usability. The proposed NARX model and three other tested models are all on the Pareto Optimal Frontier. Highlights Explores the nonlinear autoregressive with exogenous inputs (NARX) model for travel time predictions; Develops models to forecast urban arterial corridor travel time means and reliability metrics; Develops forecasting models with link-based or corridor-based travel time inputs, respectively; Evaluates the performance of various models using a Pareto Optimal Frontier; Compared travel time reliability metrics: reliability interval, 95th percentile, and buffer index.

An Eco-Friendly Multimodal Route Guidance System for Urban Areas Using Multi-Agent Technology

The use and coordination of multiple modes of travel efficiently, although beneficial, remains an overarching challenge for urban cities. This paper implements a distributed architecture of an eco-friendly transport guidance system by employing the agent-based paradigm. The paradigm uses software agents to model and represent the complex transport infrastructure of urban environments, including roads, buses, trolleybuses, metros, trams, bicycles, and walking. The system exploits live traffic data (e.g., traffic flow, density, and CO2 emissions) collected from multiple data sources (e.g., road sensors and SCOOT) to provide multimodal route recommendations for travelers through a dedicated application. Moreover, the proposed system empowers the transport management authorities to monitor the traffic flow and conditions of a city in real-time through a dedicated web visualization. We exhibit the advantages of using different types of agents to represent the versatile nature of transport networks and realize the concept of smart transportation. Commuters are supplied with multimodal routes that endeavor to reduce travel times and transport carbon footprint. A technical simulation was executed using various parameters to demonstrate the scalability of our multimodal traffic management architecture. Subsequently, two real user trials were carried out in Nottingham (United Kingdom) and Sofia (Bulgaria) to show the practicality and ease of use of our multimodal travel information system in providing eco-friendly route guidance. Our validation results demonstrate the effectiveness of personalized multimodal route guidance in inducing a positive travel behavior change and the ability of the agent-based route planning system to scale to satisfy the requirements of traffic infrastructure in diverse urban environments.

Are routes well that end well? Experienced utility of impeded simulator routes

A remaining challenge for optimizing car travel is that route assignments still contain temporary impedance. The question arises as to how the degree and the order of more and less impeding route sections influence experienced utility of routes. On driving simulator routes, participants were assigned one out of two mean speeds and one out of three segmentations (i.e., “Fast Slow,” “Constant,” and “Slow Fast”). Independent of assigned mean speed, routes with the segmentation “Slow Fast” were rated less arousing, more positive, faster and more flowing than routes with the segmentation “Fast Slow.” Further differences were revealed by an interaction between mean speed and route segmentation. In summary, optimized segmentation can increase the utility of impeded routes. This may increase the acceptance of traveler information systems and in turn subjective well-being due to more efficient travel. The study warrants further investigations of varying driving cycles under real-world conditions.

Assessing traveler compliance with the social optimum: A stated preference study

The development of Advanced Traveler Information Systems (ATIS) with en-route navigation systems has opened the door to new traffic regulation policies such as collective route guidance based on social costs, i.e. routes that minimize total travel time. However, a critical element for their efficiency is the compliance of travelers with route recommendations. It is well established that the social optimum is different from user equilibrium, where users drive on their shortest path. Thus, even when knowing that recommendations are for the common good, users may still choose not to follow the guidance given and drive as they wish. In this paper, we use a stated preference survey based on route choice situations from a real network to investigate traveler compliance for different levels of travel time sacrifice vs several social benefits. Two kinds of social benefits are considered: congestion alleviation and emission reduction. The data collected allows us to precisely quantify the travel time sacrifice that a given proportion of travelers would be ready to accept in order to take a different alternative that has a given level of social benefit. In line with the literature, our analysis confirms the decrease of compliance with the increase of sacrifice. Moreover, it suggests that the way the recommendation is intended could play a significant role in the level of compliance: the display of an advice message for the social path (in the congestion alleviation case) is shown to be more efficient than only showing additional information (in the emission reduction case).

Estimation of Travel Times for Minor Roads in Urban Areas Using Sparse Travel Time Data

Travel time is a basic measure based on which intelligent transportation systems such as traveler information systems, traffic management systems, public transportation systems are developed. Although many methodologies have been proposed, they have not yet adequately solved many challenges associated with travel time, in particular, travel time estimation for all links in a large and dynamic urban traffic network is still an open problem that needs addressing. Typically focus is placed on major roads such as motorways and main city arteries but there is an increasing need to know accurate travel times for minor urban roads. Such information is crucial for tackling air quality problems, accommodate the growing number of cars and provide accurate information for routing. This study aims to address the aforementioned challenges by introducing a methodology, namely Similar Model Searching (SMS), to estimate travel times by using historical sparse travel time data. The SMS learns the temporal and spatial relationship between the travel time of adjacent links and utilize labeled data of similar models in order to improve its overall performance. The effectiveness of the proposed method is evaluated on a section of Leicestershire traffic network in the UK. The obtained results show that SMS efficiently estimates travel time of target links using models of adjacent traffic links.

A Data-Driven Multi-Fidelity Approach for Traffic State Estimation Using Data From Multiple Sources

Accurate estimation of traffic state is crucial in the successful implementation of advanced traveler information systems and advanced traffic management systems. Currently, traffic data from various sources such as GPS, cell phones, and connected vehicles are increasingly available. Consequently, efforts have moved from estimating traffic using a single source of data to incorporating heterogeneous data, i.e. data from multiple sources, for traffic estimation. In these works, data from different sources are mostly considered to have the same level of fidelity or accuracy. This is while, in reality, different data sources may contain different levels of noise. In this work, we propose using multi-fidelity Gaussian processes to exploit data with different fidelities for traffic estimation. We demonstrate the performance of the proposed approach using a simulation study, where the results show that accounting for multi-fidelity nature of data can significantly improve the accuracy when multiple data sources are used for traffic state estimation.

An optimal global algorithm for route guidance in advanced traveler information systems

Advanced traveler information systems play essential roles in reducing traffic congestion and improving traffic efficiency. In this paper, a global algorithm for the route guidance strategy is proposed in advanced traveler information systems. Compared to the local algorithm, this global algorithm can improve the capacity and efficiency of traffic networks and also counter traffic congestion. The innovative ideas of this algorithm can be viewed mainly from three aspects. First, rather than providing a fixed path between origin and destination, the new algorithm could provide the real-time optimal route guidance information to travelers at every intersection. Moreover, because the route guidance information is based on global traffic information, the optimum global path could also be provided. Lastly, we can use this new global algorithm and apply it to many route guidance strategies. In our study, we first apply it to the congestion coefficient route guidance strategy. The simulation results demonstrate better performances of the global congestion coefficient route guidance strategy compared to that of the local congestion coefficient route guidance strategy. The advantages can be summarized as follow: (1) Critical vehicle occupancy and average flow of saturation state is doubled. (2) Critical vehicle occupancy of meta-stable state and deadlock state are raised by 2.5 times. (3) Vehicle distribution is more homogeneous. We also apply the global algorithm to the travel time route guidance strategy. Even though it brings a slightly lower average flow and a higher origin-destination travel time compared to the global congestion coefficient route guidance strategy, the global travel time route guidance strategy induces a more homogeneous vehicle distribution. Furthermore, we find that the average flow increases when information collection time period decreases, thus information should be updated in a real-time manner in efficient traffic networks.

Seoul bike trip duration prediction using data mining techniques

Trip duration is the most fundamental measure in all modes of transportation. Hence, it is crucial to predict the trip-time precisely for the advancement of Intelligent Transport Systems and traveller information systems. To predict the trip duration, data mining techniques are employed in this study to predict the trip duration of rental bikes in Seoul Bike sharing system. The prediction is carried out with the combination of Seoul Bike data and weather data. The data used include trip duration, trip distance, pickup and dropoff latitude and longitude, temperature, precipitation, wind speed, humidity, solar radiation, snowfall, ground temperature and 1-hour average dust concentration. Feature engineering is done to extract additional features from the data. Four statistical models are used to predict the trip duration. (a) Linear regression, (b) Gradient boosting machines, (c) k nearest neighbour and (d) Random Forest (RF). Four performance metrics root mean squared error, coefficient of variance, mean absolute error and median absolute error is used to determine the efficiency of the models. In comparison with the other models, the best model RF can explain the variance of 93% in the testing set and 98% (R 2) in the training set. The outcome proves that RF is effective to be employed for the prediction of trip duration.

A New Concept of Multiple Neural Networks Structure Using Convex Combination

In this article, a new concept of convex-combined multiple neural networks (NNs) structure is proposed. This new approach uses the collective information from multiple NNs to train the model. Based on both theoretical and experimental analyses, the new approach is shown to achieve faster training convergence with a similar or even better test accuracy than a conventional NN structure. Two experiments are conducted to demonstrate the performance of our new structure: the first one is a semantic frame parsing task for spoken language understanding (SLU) on the Airline Travel Information System (ATIS) data set and the other is a handwritten digit recognition task on the Mixed National Institute of Standards and Technology (MNIST) data set. We test this new structure using both the recurrent NN and convolutional NNs through these two tasks. The results of both experiments demonstrate a 4× - 8× faster training speed with better or similar performance by using this new concept.

PrePCT: Traffic congestion prediction in smart cities with relative position congestion tensor

Traffic congestion prediction is a vital part of Intelligent Transportation Systems in smart cities. Effective methods for traffic congestion prediction can help people make travel plans reasonably with Advanced Traveler Information Systems. Most of the existing methods for traffic congestion prediction was designed for a specific location. The parameters need to be modified when applying these methods to different locations. Other studies on the traffic network require sophisticated data pre-processing such as map matching. In this paper, we build a model named Relative Position Congestion Tensor and propose a Predictor for Position Congestion Tensor for traffic congestion prediction. First, we design a novel approach to construct congestion matrix on region traffic networks using the concept of relative locations for road nodes and convert matrices into three-dimensional spatio-temporal tensors. Then, we propose a method based on convolutional long-short term memory network to predict congestion at all locations of the road network in the near future. The experiments show that in all locations where congestion often occurs, the proposed method significantly outperforms baseline models including Linear Regression, Autoregressive Integrated Moving Average, Support Vector Regression, Random Forest, Gradient Boosting Regression, Long-Short Term Memory and generally outperforms the Convolution-based deep Neural Network modeling Periodic traffic data. Furthermore, we study the internal structure of the Predictor for Position Congestion Tensor model to analyze the interpretability of the model for congestion prediction. The results show that the proposed model can accurately capture the temporal and spatial characteristics of traffic.

How did Korea use technologies to manage the COVID-19 crisis? A country report

ABSTRACT This paper discusses how Korea has used technologies to respond to COVID-19. It introduces major ICT-enabled efforts for managing this infectious disease-driven crisis in four stages: screening and diagnosis (smart quarantine information system, international traveler information system, and self-health check app for entrants under special entry procedures), epidemiological investigation (epidemiological investigation system and global epidemic prevention platform for digital tracing), patient and contact management (self-quarantine safety protection app, patient management information system, and AI-based automatic counseling), and prevention (micropage, Coronamap, Coronaita, and chatbot). The Korean government’s open government principle and proactive transition toward digital transformation has led to successful control of the COVID-19 crisis.

A driving simulation study to investigate the information threshold of graphical variable message signs based on visual perception characteristics of drivers

Graphical variable message signs (VMSs) are infrastructure-based advanced traveler information systems widely used to provide drivers with real-time traffic condition information about a road section or area. However, there is a lack of research on the suitable volume of information to be shown on graphical VMSs. In practice, an overload of VMS information commonly exists, especially on China’s highways. Building on our earlier findings obtained through surveys and static cognitive experiments, this study introduces the use of dynamic simulation experiments to assess the influence of the volume of information (i.e., number of roads displayed) on graphical VMSs from the perspective of drivers’ visual perception characteristics. Thirty-two drivers participated in the driving simulation experiment and questionnaires. Five indexes, including legibility speed, legibility distance, legibility time, comprehension accuracy, and driver subjective scoring, were thoroughly analyzed to evaluate their relationships to different volumes of information (i.e., four, five, and six roads shown on a VMS). The results show that the legibility distance notably decreased with increasing volumes of information. The comprehension accuracy decreased significantly when the number of roads shown increased to six. The legibility speed, legibility time, and subjective scoring also deteriorated as the number of roads displayed on the VMS increased. The index scores were evaluated, in combination with the data of the drivers’ subjective scoring, data-based statistical analyses, and comprehensive evaluations using the TOPSIS method, to recommend that five is the recommended maximum number of roads to be shown on a graphical VMS. The results of this study support the goal of providing understandable and effective messages for drivers by addressing issues relating to how much information should be displayed on a VMS. These findings provide a basis for policy development to ensure consistent and practical designs of graphical VMSs on highways.

Multimodal Traveller Information System using both Web-Based Design Program and Traffic Analysis for Akure Metropolis

A Traveller’s Information System integrated with traffic condition which can provide vital information to the users of the system is such a powerful tool in transportation planning. In this study, web-based multimodal advanced traveller information system was developed, which supplies information to trip makers within Akure metropolis and major cities with similar traffic condition. Traffic count was also taken on five roads which most of the transportation company’s vehicle ply so as to check for the traffic condition of the roads. The system provide information such as the available route from the origin of the traveller to his or her destination, the travel time, distance of each route, closest facilities like hotels, hospitals, airline services, airport terminal, departure time, departure time, arrival time and fare. The traffic count carried out revealed that Oyemekun road has the highest volume of traffic followed by Akure-Ado road while FUTA North gate road gives the least volume of traffic. The reason for the highest volume of traffic recorded at Oyemekun road is as a result of it location close to the CBD (Central Business District) while the lowest volume of traffic recorded for FUTA North gate is as a result of it location almost at the outskirt of the town. The web-based multi-modal traveller’s information system developed will help trip makers in Akure metropolis minimize the cost of their trips, by reducing travel time, energy consumption, and transport fare. Travellers are advised to embark on their journey earlier than the peak periods so as to avoid delay.

A Multimodal Traveller Information System using both Web-Based Design Program and Traffic Analysis for Akure Metropolis

A Traveller’s Information System integrated with traffic condition which can provide vital information to the users of the system is such a powerful tool in transportation planning. In this study, web-based multimodal advanced traveller information system was developed, which supplies information to trip makers within Akure metropolis and major cities with similar traffic condition. Traffic count was also taken on five roads which most of the transportation company’s vehicle ply so as to check for the traffic condition of the roads. The system provide information such as the available route from the origin of the traveller to his or her destination, the travel time, distance of each route, closest facilities like hotels, hospitals, airline services, airport terminal, departure time, departure time, arrival time and fare. The traffic count carried out revealed that Oyemekun road has the highest volume of traffic followed by Akure-Ado road while FUTA North gate road gives the least volume of traffic. The reason for the highest volume of traffic recorded at Oyemekun road is as a result of it location close to the CBD (Central Business District) while the lowest volume of traffic recorded for FUTA North gate is as a result of it location almost at the outskirt of the town. The web-based multi-modal traveller’s information system developed will help trip makers in Akure metropolis minimize the cost of their trips, by reducing travel time, energy consumption, and transport fare. Travellers are advised to embark on their journey earlier than the peak periods so as to avoid delay.

Short-Term Traffic Delay Prediction at the Niagara Frontier Border Crossings: Comparing Deep Learning and Statistical Modeling Approaches

This study focuses on the short-term prediction of traffic delays, for passenger cars, at the three Niagara Frontier Border Crossings, namely the Peace Bridge, the Lewiston–Queenston Bridge, and the Rainbow Bridge. Predictions are made for up to 60 min into the future, using a delay dataset, collected by Bluetooth readers recently installed at these three border crossings. The delay data were first analyzed to identify the factors affecting traffic delay. Next, future delays were predicted using different deep learning techniques and statistical modeling approaches, including (1) multilayer perceptron (MLP); (2) convolutional neural network (CNN); (3) long short-term memory recurrent neural networks (LSTM RNN); (4) gated recurrent unit recurrent neural network (GRU RNN); and (5) the statistical technique known as the auto-regressive integrated moving average (ARIMA) method. A comparative analysis of the prediction accuracy of the results from the different techniques revealed that the deep learning techniques were capable of predicting border traffic delays with high accuracy, resulting in a value of the mean absolute error (MAEs) of less than 3.5 min, even when predicting delays for up to 60 min into the future. The models developed in this study can serve as a part of a traveler information system that guide travelers to the crossing with the least delay, resulting in more efficient border crossing operations.

Experimental Study of Day-to-Day Route-Choice Behavior: Evaluating the Effect of ATIS Market Penetration

This paper examines the travelers’ day-to-day route-choice behavior with Advanced Traveler Information Systems (ATIS) through laboratory-like experimental method. Five groups of route-choice behavior experiments are designed to simulate actual daily behavior of travelers. In the experiment, subjects are provided with different levels of the complete road network information to simulate the proportion of subjects equipped with ATIS equipment (i.e., ATIS market penetration) and choose the routes repeatedly. The subject’s route-choice behavior under different proportions of complete road network information is analyzed, and the strategy of releasing such complete information is determined when the performance of road network system is the best. The Braess network which consists of three routes was used in the experiment and analysis. The results show that the fluctuation of traffic flow runs through the entire experiments, but it tends to converge to user equilibrium. When the market penetration is 75%, both the fluctuation of traffic flow and the tendency of subjects to change routes are the smallest, so the road network system is the most stable. This interesting result indicates that releasing traffic information to all travelers is not the best. Other results show that the travel times of the three routes in the five groups of experiments tend to converge to and finally fluctuate around user-equilibrium travel time. With the increase in ATIS market penetration, the average travel time of subjects in each round tends to increase. The overall trend of the five groups of experiments is that as the number of route switches increases, the average travel time increases. The results also indicate that releasing traffic information to all travelers cannot weaken the Braess Paradox. On the contrary, the more travelers are provided with traffic information, the less likely it will weaken the Braess Paradox.

Towards Attention-Based Convolutional Long Short-Term Memory for Travel Time Prediction of Bus Journeys.

Travel time prediction is critical for advanced traveler information systems (ATISs), which provides valuable information for enhancing the efficiency and effectiveness of the urban transportation systems. However, in the area of bus trips, existing studies have focused on directly using the structured data to predict travel time for a single bus trip. For state-of-the-art public transportation information systems, a bus journey generally has multiple bus trips. Additionally, due to the lack of study on data fusion, it is even inadequate for the development of underlying intelligent transportation systems. In this paper, we propose a novel framework for a hybrid data-driven travel time prediction model for bus journeys based on open data. We explore a convolutional long short-term memory (ConvLSTM) model with a self-attention mechanism that accurately predicts the running time of each segment of the trips and the waiting time at each station. The model is more robust to capture long-range dependence in time series data as well.

Using Deep Time Delay Neural Network for Slot Filling in Spoken Language Understanding

Modeling the context of a target word is of fundamental importance in predicting the semantic label for slot filling task in Spoken Language Understanding (SLU). Although Recurrent Neural Network (RNN) has shown to successfully achieve the state-of-the-art results for SLU, and Bidirectional RNN is capable of obtaining further improvement by modeling information not only from the past, but also from the future, they only consider limited contextual information of the target word. In order to make the network deeper and hence obtain longer contextual information, we propose to use a multi-layer Time Delay Neural Network (TDNN), which is prevalent in current large vocabulary continuous speech recognition tasks. In particular, we use a TDNN with symmetric time delay offset. To make the stacked TDNN easily trained, residual structures and skip concatenation are adopted. In addition, we further improve the model by introducing ResTDNN-BiLSTM, which combines the advantages of both the residual TDNN and BiLSTM. Experiments on slot filling tasks on the Air Travel Information System (ATIS) and Snips benchmark datasets show the proposed SC-TDNN-C achieves state-of-the-art results without any additional knowledge and data resources. Finally, we review and compare slot filling results by using a variety of existing models and methods.

Network‐wide traffic speed forecasting: 3D convolutional neural network with ensemble empirical mode decomposition

AbstractAccurate traffic speed forecasting is one of the most critical tasks in proactive traffic management and the deployment of advanced traveler information systems. This paper proposes a hybrid forecasting approach named DeepEnsemble by integrating the three‐dimensional convolutional neural network (3D CNN) with ensemble empirical mode decomposition (EEMD). There are four steps in this hybrid approach. First, EEMD is adopted to decompose the complex traffic speed time series data with noise into several intrinsic mode functions (IMFs) and a residue. Second, a three‐dimensional tensor is established and fed into 3D CNN for prediction. Third, the output of 3D CNN prediction is obtained by a linear combination of the results of all components. Finally, the 3D CNN prediction output, external features, and historical features are fused to predict the network‐wide traffic speed simultaneously. The proposed DeepEnsemble approach is tested on the three‐month traffic speed series data of a real‐world large‐scale urban expressway network with 308 traffic flow detectors in Beijing, China. The experimental results indicate that DeepEnsemble outperforms the state‐of‐the‐art network‐wide traffic speed forecasting models. 3D CNN learns temporal, spatial, and depth information better than 2D CNN. Moreover, forecasting accuracy can be improved by employing EEMD. DeepEnsemble is a promising model with scalability and portability for network‐wide traffic speed prediction and can be further extended to conduct traffic status monitoring and congestion mitigation strategies.