Academic literature on the topic 'Multimodal traffic behavior'

The rapid aggregation of modern urban population and the rapid growth of car travel lead to traffic congestion, environmental pollution, and other problems. In view of the limited land resources in our country, it is impractical to meet residents’ travel demand by blindly increasing traffic supply. Therefore, addressing the urban road congestion problem for sustainable development of modern cities, the paper makes research on residents’ travel behavior characteristics and travel preference under the condition of multimodal transportation to formulate reasonable traffic demand management strategy for the guide on public traffic demand, bus priority strategy, and congestion management. The operation characteristic of each transportation mode is analyzed by comparing its related traffic and economic characteristics. Multimode traffic choice behavior is discussed by establishing multiple logistic regression models to analyze the main influencing factors to travelers’ social and economic attributes, travel characteristics, and preference based on travel survey data of urban residents. The paper proposes the development of an urban public transportation system and travelling mode shift from cars to public transportation as reasonable travel structure for congestion management and sustainable development of modern cities.

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.

For mobile robots navigating on sidewalks, the ability to safely cross street intersections is essential. Most existing approaches rely on the recognition of the traffic light signal to make an informed crossing decision. Although these approaches have been crucial enablers for urban navigation, the capabilities of robots employing such approaches are still limited to navigating only on streets that contain signalized intersections. In this article, we address this challenge and propose a multimodal convolutional neural network framework to predict the safety of a street intersection for crossing. Our architecture consists of two subnetworks: an interaction-aware trajectory estimation stream ( interaction-aware temporal convolutional neural network (IA-TCNN)), that predicts the future states of all observed traffic participants in the scene; and a traffic light recognition stream AtteNet. Our IA-TCNN utilizes dilated causal convolutions to model the behavior of all the observable dynamic agents in the scene without explicitly assigning priorities to the interactions among them, whereas AtteNet utilizes squeeze-excitation blocks to learn a content-aware mechanism for selecting the relevant features from the data, thereby improving the noise robustness. Learned representations from the traffic light recognition stream are fused with the estimated trajectories from the motion prediction stream to learn the crossing decision. Incorporating the uncertainty information from both modules enables our architecture to learn a likelihood function that is robust to noise and mispredictions from either subnetworks. Simultaneously, by learning to estimate motion trajectories of the surrounding traffic participants and incorporating knowledge of the traffic light signal, our network learns a robust crossing procedure that is invariant to the type of street intersection. Furthermore, we extend our previously introduced Freiburg Street Crossing dataset with sequences captured at multiple intersections of varying types, demonstrating complex interactions among the traffic participants as well as various lighting and weather conditions. We perform comprehensive experimental evaluations on public datasets as well as our Freiburg Street Crossing dataset, which demonstrate that our network achieves state-of-the-art performance for each of the subtasks, as well as for the crossing safety prediction. Moreover, we deploy the proposed architectural framework on a robotic platform and conduct real-world experiments that demonstrate the suitability of the approach for real-time deployment and robustness to various environments.

Traffic congestion brings more and more negative effects on the urban economy and society safety. This paper analyzes and extends the travel mode split problem for urban multimodal networks based on the game theory and Arnotts bottleneck model. Firstly, we present a theoretical model of traffic behavior under various tolling schemes such as no-toll and dynamic toll. Secondly, several important behavioral features related to the response of users to the congestion pricing strategy are provided. Such features include the travelers relative valuations of early arrival, late arrival, and the bus conditions. Finally, we put forward numerical examples to explain the model.

Advanced traveler information systems provide travelers with pre-trip and en route travel information necessary to improve the trip decision making process based on various criteria (e.g., avoiding the negative impacts of traffic congestion, selecting specific travel modes, etc.). This study investigates an adaptive routing methodology for multimodal transportation networks. To integrate transit networks, the model takes into account both the predefined timetables of public transportation services and the variability of travel times. A graph theory based methodology is proposed to capture travel behavior within a multimodal network. The study advances a routing algorithm based on Markov decision processes. Special network modeling elements were defined to allow the developed algorithm to select the most efficient transportation mode at each junction along a given route. The proposed methodology is applied to a small real-world network located in the central business district area of Montreal, Quebec. The network includes bus, subway, and bicycle transportation facilities. The simulations were run under the assumption that users do not use private vehicles to travel between arbitrary selected origin and destination points. The developed routing algorithm was applied to several simulation scenarios. The results identified what is the most efficient combination of transportation modes that the travelers have to use given certain traffic and transit service conditions. Larger and more complex networks of motorized and non-motorized modes with stochastic properties will be investigated in subsequent work.

As a multimodal travel behavior, park and ride includes several trip modes such as car, walking, bus, or railway. And people’s choice of park and ride is influenced by many factors. This paper, based on the park and ride behavior survey in Beijing, will analyze the relationship between the perception of the influencing factors and the behavior intent for park and ride by using structural equation modeling. The conclusions suggest that the park and ride choice for travelers is a passive behavior which means giving up driving the car is mainly caused by the serious traffic congestion. Furthermore, improving the service level of the park and ride facilities and the comfort for riding bus or railway will increase the utilization of park and ride facilities. The perceptions of the influencing factors have both direct and indirect effects on the travel intent for park and ride by the interaction among the influencing factors.

Real-time traffic signal control presents a challenging multiagent planning pro­blem, particularly in urban road networks where, unlike simpler arterial settings, there are competing dominant traffic flows that shift through the day. Further complicating matters, urban environments require attention to multimodal traffic flows (vehicles, pedestrians, bicyclists, buses) that move at different speeds and may be given different priorities. For the past several years, my research group has been developing and refining a real-time, adaptive traffic signal control system to address these challenges, referred to as scalable urban traffic control (Surtrac). Combining principles from automated planning and scheduling, multiagent systems, and traffic theory, Surtrac treats traffic signal control as a decentralized online planning process. In operation, each intersection repeatedly generates and executes (in rolling horizon fashion) signal-timing plans that optimize the movement of currently sensed approaching traffic through the intersection. Each time a new plan is produced (nominally every couple of seconds), the intersection communicates to its downstream neighbors what traffic it expects to send their way, allowing intersections to construct longer horizon plans and achieve coordinated behavior. Initial evaluation of Surtrac in the field has demonstrated significant performance improvements, and the technology is now deployed and operating in several U.S. cities. More recent work has focused on integrating real-time adaptive signal control with emerging connected vehicle technology, and exploration of the opportunities for enhanced mobility that direct vehicle (or pedestrian) to infrastructure communication can provide. Current technology development efforts center on vehicle route sharing, smart transit priority, safe intersection crossing for pedestrians with disabilities, real-time incident detection, and integrated optimization of signal control and route choice decisions. This article provides an overview of this overall research effort.

Background:Active travel has been linked with improved transportation and health outcomes, such as reduced traffic congestion and air pollution, improved mobility, accessibility, and equity, and increased physical and mental health. The purpose of this study was to better understand active travel characteristics, motivators, and deterrents in the El Paso, TX, region.Methods:A multimodal transportation survey brought together elements of transportation and health, with a focus on attitudinal characteristics. The analysis consisted of an initial descriptive analysis, spatial analysis, and multivariate binary and ordered-response models of walking and bicycling behavior.Results:The motivators and deterrents of active travel differed for walkers, bicyclists, and noncyclists interested in bicycling. The link between active travel and life satisfaction was moderated by age, with a negative association for older travelers. This effect was stronger for bicycling than it was for walking.Conclusions:Based on the findings, several interventions to encourage walking and bicycling were suggested. These included infrastructure and built environment enhancements, workplace programs, and interventions targeting specific subpopulations.

Multimodalität, die Nutzung mehrerer Verkehrsmittel innerhalb eines bestimmten Zeitraums, ist ein Sammelbegriff für sehr unterschiedlich in der Alltagspraxis umgesetztes Mobilitätsverhalten. Sie wird als Gegenkonzept zur einseitigen Nutzung des privaten Autos verstanden, mit dem sich große Hoffnungen für die zukünftige Entwicklung des Verkehrs verbinden. Bisherige Arbeiten grenzen den betrachteten Personenkreis fast immer auf eine bestimmte Form multimodalen Verhaltens ein, allen voran auf die Nutzung des Autos und öffentlicher Verkehrsmittel. Ansatzpunkt der vorliegenden Arbeit ist es, die verschiedenen Facetten multimodalen Verhaltens in ihrer Gesamtheit darzustellen und zu untersuchen. Hierzu wird eine Klassifikation entwickelt, die sich aus der Modalwahl ableitet. Die Analyse des Mobilitätsverhaltens basiert auf den Daten des Deutschen Mobilitätspanels von 1999 bis 2008 und der Studie Mobilität in Deutschland aus den Jahren 2002 und 2008. In Abhängigkeit davon, welche der Verkehrsmittel MIV, ÖV und Fahrrad im Verlauf einer Woche zum Einsatz kommen, werden die Probanden einer Modalgruppe zugeordnet. Die Analyse lässt den enormen Facettenreichtum multimodalen Verhaltens erkennen. Generell ist multimodales Verhalten eine urbane Verhaltensweise, die v.a. den Alltag junger Personen prägt und dies in zunehmendem Maß. In Summe legen Multimodale weniger Kilometer mit dem Auto zurück als monomodale Autofahrer. Ihr CO2-Fußabdruck fällt je nach Datensatz um 20 bis 34 Prozent geringer aus als der von ausschließlichen Autofahrern. Nichtsdestotrotz nutzen viele Multimodale das Auto für einen erheblichen Anteil ihrer Wege. In Zukunft wird ausschlaggebend sein, wie sich die Verkehrsmittelanteile v.a. in Bezug auf weite Wege verändern und wie sich die gegenwärtig auf der Nachfrage- und Angebotsseite feststellbaren Veränderungen auswirken.<br>Multimodality, the use of several modes of transportation during a specified time period, is a general term for a wide variety of everyday mobility behaviors. It is perceived as an alternative to one-sided use of private cars, and one which has attracted great hopes for the future development of transportation. Based on the research which has been done in the past, people almost always limit themselves to a particular form of multimodal behavior, most often to use of cars and public transportation. The starting point of the present paper is to present and examine the various facets of multimodal behavior in their entirety. To this end, a method of classification will be developed which is derived from the selection of modes of transportation. The analysis of mobility behavior will be based on the data of the German Mobility Panel from 1999 to 2008 and the Mobility in Germany study from the years 2002 and 2008. Subjects will be assigned to modal groups depending on which of the modes of transportation, motorized individual traffic, public transportation and bicycle, are used in the course of a week. The analysis reveals the enormously diverse nature of multimodal behavior. In general, multimodal behavior is an urban phenomenon which is increasingly characterizing the everyday urban routine, especially for younger persons. In aggregate, multimodal persons drive fewer kilometers by car than monomodal car drivers. Their carbon footprint is 20-34 percent less than that of exclusive car drivers, depending on the data set. Nevertheless, many multimodal persons do use cars for a considerable portion of their travel needs. How the relative share of the various modes of transportation will change in the future, especially with respect to long-distance travel, and the impact of the currently observable changes in supply and demand will be decisive factors in the future.

We investigate the application of methodologies for the analysis of complex networks to understand the properties of systems of systems in a cybersecurity context. We are interested to resilience and attribution: the first relates to the behavior of the system in case of faults/attacks, namely to its capacity to recover full or partial functionality after a fault/attack; the second corresponds to the capability to tell faults from attacks, namely to trace the cause of an observed malfunction back to its originating cause(s). We present experiments to witness the effectiveness of our methodology considering a discrete event simulation of a multimodal logistic network featuring 40 nodes distributed across Italy and a daily traffic roughly corresponding to the number of containers shipped through in Italian ports yearly, averaged on a daily basis.