Identifying Atypical Travel Patterns for Improved Medium-Term Mobility Prediction

Perspectives of the use of smartphones in travel behaviour studies: Findings from a literature review and a pilot study

Prelims

For several years, the United Kingdom (UK) transport policy has discouraged the construction of additional road space to meet the rising demand for road travel. Increasingly, Local Traffic Authorities (LTAs) in England are looking towards technology and Intelligent Transport Systems (ITS) for maximizing the efficiency with which they use their road networks. The aim of this paper is twofold. First, it provides a perspective on how English LTAs are using and planning to use ITS. Primarily, the recent UK Traffic Management Act influences these new strategies, which include the types of technology they are choosing. Findings are presented from some research carried out with a variety of English LTAs examining their use of and opinions concerning technology and ITS. Second, a case study is then provided on how one English LTA (Surrey County Council) is deploying ITS to help meet its new obligations. Conclusions are then drawn on the efficacy of setting central policy to influence LTAs in this manner.

With the fast development of data acquisition techniques and storage capabilities, more and more human travel related data has been collected. These “Big Data” bring both opportunities and challenges for human travel behavior research. In this paper, millions of residents' daily mobile phone records collected in Shanghai are analyzed by four visualization methods to characterize urban human travel behavior patterns. Particularly, this paper studies: 1) the travel OD map that reveals the spatial structure of travel demand, 2) the two-dimensional population density map that characterizes the continuous spatial distribution of residents, 3) the vector field map of resident movement that depicts the local movement feature of residents, 4) the particle graph of resident flow that demonstrates the smooth dynamic movement process of residents. Both the design objectives and the animation techniques for each visualization method are discussed. The findings on residents' travel behaviors could help the government make the appropriate urban planning and help residents make the better daily travel planning.

The emerging Omicron variant poses a serious threat to human health. Public transports play a critical role in infection spread. Based on the data of nearly 4 billion smartcard uses, between January 1, 2019 and January 31, 2021 from the Mass Transit Railway Corporation of Hong Kong, we analyzed the subway travel behavior of different population groups (adults, children, students and senior citizens) due to the COVID-19 pandemic and human travel behavior under different interventions (e.g. work suspension, school closure). Due to the pandemic, the number of MTR passengers (the daily number of passengers in close proximity in subway carriages) decreased by 37.4% (40.8%) for adults, 80.3% (78.5%) for children, 71.6% (71.6%) for students, and 33.5% (36.1%) for senior citizens. Due to work from home (school suspension), the number of contacted adults (students/children) in the same carriage during the rush hours decreased by 39.6% (38.6%/43.2%). If all workers, students, and children were encouraged to commute avoiding rush hours, the possible repeated contacts during rush hour of adults, children and students decreased by 73.3%, 77.9% and 79.5%, respectively. Since adults accounted for 87.3% of the total number of subway passengers during the pandemic, work from home and staggered shift pattern of workers can reduce the infection risk effectively. Our objective is to find the changes of local travel behavior due to the pandemic. From the perspective of public transports, the results provide a scientific support for COVID-19 prevention and control in cities.

Intelligent Transportation Systems (ITS) are emerging field characterized by complex data model, dynamics and strict time requirements. Ensuring cybersecurity in ITS is a complex task on which the safety and efficiency of transportation depends. The imposition of standards for a comprehensive architecture, as well as specific security standards, is one of the key steps in the evolution of ITS. The article examines the general outlines of the ITS architecture and security issues. The main focus of security approaches is: configuration and initialization of the devices during manufacturing at perception layer; anonymous authentication of nodes in VANET at network layer; defense of fog-based structures at support layer and description and standardization of the complex model of data and metadata and defense of systems, based on AI at application layer. The article oversees some conventional methods as network segmentation and cryptography that should be adapted in order to be applied in ITS cybersecurity. The focus is on innovative approaches that have recently been trying to find their place in ITS security strategies. These approaches includes blockchain, bloom filter, fog computing, artificial intelligence, game theory and ontologies. In conclusion, a correlation is made between the commented methods, the problems they solve and the architectural layers in which they are applied.

BackgroundBehavioral science researchers are increasingly collecting detailed location data such as second-by-second GPS tracking on participants due to increased ease and affordability. While intraindividual variability has been discussed in the travel literature for decades, traditional methods designed for studying individual differences in central tendencies limit the extent to which novel questions about variability in lived experiences can be answered. Thus, new methods of quantifying behavior that focus on intraindividual variability are needed to address the context in which the behavior occurs and the location tracking data from which behavior is derived.MethodsWe propose deviations from typical paths as a data processing technique to separate individual-level typical travel behavior from a location tracking data set in order to highlight atypical travel behavior as an outcome measure.ResultsA simulated data example shows how the method works to produce deviation measures from a location dataset. Analysis of these deviations offers additional insights compared to traditional measures of maximum daily distance from home.ConclusionsThis process can be integrated into larger research questions to explore predictors of atypical behavior and potential mechanisms of behavior change.

Half of the world’s population resides in cities and this percentage is going to rise in the coming years. This rapid transformation of the economy and society is making traditional city management redundant. Intensive urbanization has brought with it some challenges. Scanty resources, population growth, increased carbon footprint, reduced air quality, congestion on roads, depriving health status, and falling food quality, all are prominent parts of cities. Does this bring a question in mind is accelerated growth sustainable? The answer and solution to this is an adaption of the Smart City Concept. A smart city integrates all the available technology with the requirements of city development and builds infrastructure which can improve the quality of life of citizens. Smart cities are designed in a manner to use minimum resources with maximum efficiency in all areas of urban life. It provides smart solutions using smart technologies. Urban needs and technological advancements meet in smart cities. It manages cities more quickly and effectively by using AI, IT technologies, and robotic process automation. Intelligent transportation, intelligent energy, intelligent governance, intelligent security, intelligent healthcare are all central to the idea of smart cities. The emphasis of this essay is on smart transit as a component of smart cities. The movement of people and goods across several geographical boundaries has been significantly aided by the transportation sector. Globally, the majority of smart cities face logistical and transportation issues. Both the fast growing human population and the increase in the number of cars on the road are to blame for this. In order to design and run a sustainable transportation system, technology may be of great assistance. Robotic systems for real-time vehicle data collection, traffic management, and using mobility on demand in trip planning through a single user interface have emerged as cities struggle with traffic congestion. The transportation industry, which faces difficulties including traffic congestion, unanticipated delays, and routing issues that cause businesses to lose money, has successfully used robotic process automation. Robotic process automation provides reliable results and affordable solutions while reducing uncertainty in the decision-making process. It is very helpful in designing and running a successful transit system. Robotic Process Automation (RPA) systems for obtaining real-time data from vehicles, buses, and trucks for traffic management and using mobility on demand in trip planning through a single user interface have emerged as cities struggle with poor management and congestion. By examining different uses of robotic process automation, it is possible to evaluate precise prediction and detection models that are intended to forecast traffic volume, an appropriate route, traffic conditions, accurate information about timing, incidents, and which time and roadway for transport are suitable for running and mobility. The application of RPA in smart cities could be seen in Intelligent Transportation Systems’ Traffic Management, Public Transportation, Safety Management, Manufacturing, and Logistics. The research focuses on areas of concern for smart cities in the sphere of the transportation sector and related difficulties that may be solved via robotic process automation. The article emphasizes the challenges and solutions for making smart transportation more effective by using Intelligent Robotic Transportation Systems.

In traditional intelligent transportation system (ITS), the information source is often collected from a single view. However, as ITS becoming increasingly complicated, there is a need to describe one object from several different information views/domains. Information from multiple sources can provide extra worthwhile information for ITS users especially in cloud environment. Moreover, existing local ITSs usually provide information by fixed algorithms, which is not adequate to the dynamic transportation scenarios that produce big traffic data with time series. In this paper, we propose a complete self-adaptive multi-view framework for multi-source information service in cloud ITS, which mainly consists of a Newton multi-parameter optimization, a multi-layer feed-forward neural network and a finite multi-view mixture distribution. A simulation on real-world application, with six different types of information views, demonstrates the underlying effectiveness of the proposed framework.

Comprised of a broad range of diverse technologies, including information processing, communications, control, and electronics, Intelligent Transportation Systems (ITS) were first introduced as a means to solve the conflict between constantly increasing travel demand and insufficient transportation infrastructure. However, the impact of ITS on road safety was soon realized, and it became one of the major purposes of some ITS systems instead of only a side product. This paper summarizes the impacts of ITS technologies on road safety and the challenges of ITS safety evaluation. Based on the evaluation of collected ITS benefits information, this paper concludes that a variety of ITS technologies have the potential to make a substantial contribution to road safety and thereby have a substantial positive economic impact in the United States.

Advanced traveler information systems (ATIS) are one component of intelligent transportation systems (ITS), and a major component of ATIS is travel time information. Automatic vehicle location (AVL) systems, which are a part of ITS, have been adopted by many transit agencies to track their vehicles and to predict travel time in real time. Because of the complexity involved, there is no universally adopted approach for this latter application, and research is needed in this area. The objectives of the research in this paper are to develop a model to predict bus arrival time using AVL data and apply the model for real-time applications. The test bed was a bus route located in Houston, Texas, and the travel time prediction model considered schedule adherence, traffic congestion, and dwell times. A historical data-based model, regression models, and artificial neural network (ANN) models were used to predict bus arrival time. It was found that ANN models outperformed both the historical data-based model and the regression model in terms of prediction accuracy. It was also found that the ANN models can be used for real-time applications.

Analyzing and mining trajectories of moving objects (such as persons or vehicles) in the cities bring a promising way to discover the potential knowledge and therefore can foster diversified applications, including personalized travel services, intelligent transportation systems (ITSs), and etc. For increasing the intelligence of current public transit systems, the paper proposes to discover and utilize the patterns in passenger trajectory streams to optimize bus scheduling. More specifically, the paper first analyzes the real world data from bus smart cards so as to fully understand the nature of passenger trajectories and bus operations. Based on it, the paper defines a new trip pattern, i.e., the frequent bus passenger trip pattern for bus scheduling (the FBPT4BS pattern in short). Then, the paper proposes an approach. The approach gives the procedure of discovering FBPT4BS patterns from passenger trajectory streams and finds the bus lines whose capacities are not enough to satisfy the passengers' travel demands. Further, the approach gives the suggestion on the corresponding scheduling adjustment strategy for bus lines. Experiments are conducted on the data from the Beijing Public Transport Group. The experimental results show that the proposed approach can efficiently decrease the travel times of passengers.

Computer simulation method has been used to measure the effect of new Intelligent Transportation Systems (ITS). Prior works which make use of the method simulated simplified travel demand, thus it is necessary to represent the demand correctly. Most of existing researches for forecasting travel behaviors need survey data of travel activity in target city. The data is called Person Trip (PT) data. Therefore, they are not able to be applied to cities where the survey was not conducted. In this paper, we propose a method for modeling and estimating travel behaviors, using Bayesian network (BN). BN is constructed based on dependency zone and trip characteristics. The zones are characterized by the important facilities for travelers. Our method is able to apply to the cities since the zone characteristics are available without PT data. In addition, the dependency is represented as graph structure obtained by using K2 algorithm. Our experimental results show the effectiveness of our method for estimating the behaviors.

Data Fusion for ITS: Techniques and Research Needs

114 Road traffic injury prevention: the role of artificial intelligence