282 research outputs found
Information and Communication Technologies(ICT), Activity Decisions,and Travel Choices: 20 years into the Second Millennium and where do we go next?
CENTENNIAL PAPERSStanding Committee on Effects of Information and Communication Technologies (ICT) on Travel Choices (ADB20)Giovanni Circella, ChairInformation and Communication Technologies(ICT), Activity Decisions,and Travel Choices: 20 years into the Second Millennium and where do we go next?JACEKPAWLAK,Imperial College LondonGIOVANNICIRCELLA, University of California, Davis andGeorgia Institute of TechnologyHANIS.MAHMASSANI, Northwestern UniversityPATRICIAL.MOKHTARIAN, Georgia Institute of TechnologyABSTRACTInformation and Communication Technologies, or ICT,have rapidly emerged asan integral element of everyday life, interactingin an essential manner with mobility and the activity patterns that engender it. The current paper reflects uponthistrendandthe opportunities and challenges itrepresents.Givenmore than three decades of research in the domain of interactions between ICT, activity decisions and travel choices, we acknowledgethe elaborate, disruptiveand oftenunexpected waysalong which ICT interact with society.Tosupport the objective of theADB20 Committee, namely tosupportand promote theemerging research questions, we identifya number of technological, societal and behavioral trends related to ICT and mobility that are likelyto be major driving forces for activity-travel behavior considerations in the next 15 years. Those include democratization of technology; personalization; shared and commoditized mobility; automation;data as the new currency; next generation connectivity, including 5G; evolving social media and socialization; new forms of shopping; digital twins;activity fragmentation; andmultitasking.We also observe that inevitably, theincreasingly interlocking relationshipbetween ICT and mobility will bring challengesrelated to balancing efficiency vs. redundancy and resilience, ensuring transparency, susceptibility to malicious activitiesandtackling the digital divide. We argue that those should not be seen as barriers to realization of the ultimate benefits for society, providing that thetransportation research agenda maintains focus on the evolution of ICTand rigorously explores the related impacts on activity decisions, travel choices and, more broadly, on transportationsystems
Modeling Lane-Changing Behavior in a Connected Environment: A Game Theory Approach
AbstractVehicle-to-Vehicle communications provide the opportunity to create an internet of cars through the recent advances in communication technologies, processing power, and sensing technologies. Aconnected vehicle receives real-time information from surrounding vehicles; such information can improve drivers’ awareness about their surrounding traffic condition and lead to safer and more efficient driving maneuvers. Lane-changing behavior,as one of the most challenging driving maneuvers to understand and to predict, and a major source of congestion and collisions, can benefit from this additional information.This paper presents a lane-changing model based on a game-theoretical approach that endogenously accounts for the flow of information in a connected vehicular environment.A calibration approach based on the method of simulated moments is presented and a simplified version of the proposed framework is calibrated against NGSIM data. The prediction capability of the simplified model is validated. It is concluded the presented framework is capable of predicting lane-changing behavior with limitations that still need to be addressed.Finally, a simulation framework based on the fictitious play is proposed. The simulation results revealed that the presented lane-changing model provides a greater level of realism than a basic gap-acceptance model
Characteristics of Vehicular Traffic Flow at a Roundabout
We construct a stochastic cellular automata model for the description of
vehicular traffic at a roundabout designed at the intersection of two
perpendicular streets. The vehicular traffic is controlled by a self-organized
scheme in which traffic lights are absent. This controlling method incorporates
a yield-at-entry strategy for the approaching vehicles to the circulating
traffic flow in the roundabout. Vehicular dynamics is simulated within the
framework of the probabilistic cellular automata and the delay experienced by
the traffic at each individual street is evaluated for specified time
intervals. We discuss the impact of the geometrical properties of the
roundabout on the total delay. We compare our results with traffic-light
signalisation schemes, and obtain the critical traffic volume over which the
intersection is optimally controlled through traffic light signalisation
schemes.Comment: 10 pages, 17 eps figures. arXiv admin note: text overlap with
arXiv:cond-mat/040107
A Simplified Cellular Automaton Model for City Traffic
We systematically investigate the effect of blockage sites in a cellular
automaton model for traffic flow. Different scheduling schemes for the blockage
sites are considered. None of them returns a linear relationship between the
fraction of ``green'' time and the throughput. We use this information for a
fast implementation of traffic in Dallas.Comment: 12 pages, 18 figures. submitted to Phys Rev
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Least expected time paths in stochastic, time-varying transportation networks
The authors consider stochastic, time-varying transportation networks, where the arc weights (arc travel times) are random variables with probability distribution functions that vary with time. Efficient procedures are widely available for determining least time paths in deterministic networks. In stochastic but time-invariant networks, least expected time paths can be determined by setting each random arc weight to its expected value and solving an equivalent deterministic problem. This paper addresses the problem of determining least expected time paths in stochastic, time-varying networks. Two procedures are presented. The first procedure determines the a priori least expected time paths from all origins to a single destination for each departure time in the peak period. The second procedure determines lower bounds on the expected times of these a priori least expected time paths. This procedure determines an exact solution for the problem where the driver is permitted to react to revealed travel times on traveled links en route, i.e. in a time-adaptive route choice framework. Modifications to each of these procedures for determining least expected cost (where cost is not necessarily travel time) paths and lower bounds on the expected costs of these paths are given. Extensive numerical tests are conducted to illustrate the algorithms` computational performance as well as the properties of the solution
An agent-based approach to assess drivers’ interaction with pre-trip information systems.
This article reports on the practical use of a multi-agent microsimulation framework to address the issue of assessing drivers’
responses to pretrip information systems. The population of drivers is represented as a community of autonomous agents,
and travel demand results from the decision-making deliberation performed by each individual of the population as regards
route and departure time. A simple simulation scenario was devised, where pretrip information was made available to users
on an individual basis so that its effects at the aggregate level could be observed. The simulation results show that the
overall performance of the system is very likely affected by exogenous information, and these results are ascribed to demand
formation and network topology. The expressiveness offered by cognitive approaches based on predicate logics, such as the
one used in this research, appears to be a promising approximation to fostering more complex behavior modelling, allowing
us to represent many of the mental aspects involved in the deliberation process
Volatile Decision Dynamics: Experiments, Stochastic Description, Intermittency Control, and Traffic Optimization
The coordinated and efficient distribution of limited resources by individual
decisions is a fundamental, unsolved problem. When individuals compete for road
capacities, time, space, money, goods, etc., they normally make decisions based
on aggregate rather than complete information, such as TV news or stock market
indices. In related experiments, we have observed a volatile decision dynamics
and far-from-optimal payoff distributions. We have also identified ways of
information presentation that can considerably improve the overall performance
of the system. In order to determine optimal strategies of decision guidance by
means of user-specific recommendations, a stochastic behavioural description is
developed. These strategies manage to increase the adaptibility to changing
conditions and to reduce the deviation from the time-dependent user
equilibrium, thereby enhancing the average and individual payoffs. Hence, our
guidance strategies can increase the performance of all users by reducing
overreaction and stabilizing the decision dynamics. These results are highly
significant for predicting decision behaviour, for reaching optimal behavioural
distributions by decision support systems, and for information service
providers. One of the promising fields of application is traffic optimization.Comment: For related work see http://www.helbing.or
Criterion for traffic phases in single vehicle data and empirical test of a microscopic three-phase traffic theory
A microscopic criterion for distinguishing synchronized flow and wide moving
jam phases in single vehicle data measured at a single freeway location is
presented. Empirical local congested traffic states in single vehicle data
measured on different days are classified into synchronized flow states and
states consisting of synchronized flow and wide moving jam(s). Then empirical
microscopic characteristics for these different local congested traffic states
are studied. Using these characteristics and empirical spatiotemporal
macroscopic traffic phenomena, an empirical test of a microscopic three-phase
traffic flow theory is performed. Simulations show that the microscopic
criterion and macroscopic spatiotemporal objective criteria lead to the same
identification of the synchronized flow and wide moving jam phases in congested
traffic. It is found that microscopic three-phase traffic models can explain
both microscopic and macroscopic empirical congested pattern features. It is
obtained that microscopic distributions for vehicle speed difference as well as
fundamental diagrams and speed correlation functions can depend on the spatial
co-ordinate considerably. It turns out that microscopic optimal velocity (OV)
functions and time headway distributions are not necessarily qualitatively
different, even if local congested traffic states are qualitatively different.
The reason for this is that important spatiotemporal features of congested
traffic patterns are it lost in these as well as in many other macroscopic and
microscopic traffic characteristics, which are widely used as the empirical
basis for a test of traffic flow models, specifically, cellular automata
traffic flow models.Comment: 27 pages, 16 figure
Computation of Equilibrium Distributions of Markov Traffic-Assignment Models
Markov traffic-assignment models explicitly represent the day-to-day evolving interaction between traffic congestion and drivers' information acquisition and choice processes. Such models can, in principle, be used to investigate traffic flows in stochastic equilibrium, yielding estimates of the equilibrium mean and covariance matrix of link or route traffic flows. However, in general these equilibrium moments cannot be written down in closed form. While Monte Carlo simulations of the assignment process may be used to produce “empirical” estimates, this approach can be extremely computationally expensive if reliable results (relatively free of Monte Carlo error) are to be obtained. In this paper an alternative method of computing the equilibrium distribution is proposed, applicable to the class of Markov models with linear exponential learning filters. Based on asymptotic results, this equilibrium distribution may be approximated by a Gaussian process, meaning that the problem reduces to determining the first two multivariate moments in equilibrium. The first of these moments, the mean flow vector, may be estimated by a conventional traffic-assignment model. The second, the flow covariance matrix, is estimated through various linear approximations, yielding an explicit expression. The proposed approximations are seen to operate well in a number of illustrative examples. The robustness of the approximations (in terms of network input data) is discussed, and shown to be connected with the “volatility” of the traffic assignment process
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