Risk attitude reversals in drivers' route choice when range of travel time information is provided

Automobile drivers were recently found to be risk averse when choosing among routes that had an average travel time shorter than the certain travel time of a route considered as a reference. Conversely, drivers were found to be risk seeking when choosing among routes that had an average travel time longer than the certain travel time of the reference route. In a driving simulation study in which the reference route had a range of travel times, this pattern was replicated when thereference range was smaller than the ranges of the available routes. However, the pattern was reversed when the reference range was larger than the ranges of the available routes. We recently proposed a simple heuristic model that fit the relatively complex data quite well. Actual or potential applications of this research include the design of variable message signs and of route choice support systems.     

Combined Activity/Travel Choice Models: Time-Dependent and Dynamic Versions

To fully understand and predict travel demand and traffic flow, it is necessary to investigate what drives people to travel. The analysis should examine why, where and when various activities are engaged in, and how activity engagement is related to the spatial and institutional organization of an urban area. In view of this, two combined activity/travel choice models are presented in this paper. The first one is a time-dependent (quasi-dynamic) model for long-term transport planning such as travel demand forecasting, while the other one is a dynamic model for short-term traffic management such as instantaneous flow analysis. The time-dependent model is formulated as a mathematical programming problem for modeling the multinomial logit activity/destination choice and the user equilibrium route choice behavior. It can further be converted to a variational inequality problem. On the other hand, the dynamic model is aimed to find a solution for equilibrium activity location, travel route and departure time choices in queuing networks with multiple commuter classes. It is formulated as a discrete-time, finite-dimensional variational inequality and then converted to an equivalent zero-extreme value minimization problem. Solution algorithms are proposed for these two models and numerical example is presented for the latter. It is shown that the proposed modeling approaches, either based on time-dependent or dynamic traffic assignment principles, provide powerful tools to a wide variety of activity/travel choice problems in dynamic domain.     

Pedestrian Travel Behavior Modeling

This paper presents a dynamic mixed discrete-continuous choice approach to modeling pedestrian travel and activity choice behavior in public facilities. The approach views revealed behavior as a manifestation of pedestrians’ preferences by assuming that pedestrians choose the alternative that maximizes expected (subjective) utility, while taking into account the uncertainty in expected traffic conditions. The choice dimensions are trajectories between origin and subsequent destinations, areas where activities are performed (multiple vs. fixed destination), execution of discretionary activities, and finally activities completion times and order.The disutility of a trajectory determines the trajectory choice of the traveler. Destination area choice is included in the modeling by determining time-dependent and destination-specific arrival costs. Furthermore, penalties for not executing a planned activity are introduced into the modeling framework. The resulting modeling approach has a clear analogy with stochastic control theory and dynamic programming in continuous time and space.The main innovations presented here is the relaxation of the assumption that routes are discrete sets of travel links. The approach relaxes the need to build a discrete network, while routes (trajectories) are continuous functions in time and space. At the same time, destination choice is included in the modeling framework.     

Travel route recommendation using geotagged photos

We propose a travel route recommendation method that makes use of the photographers’ histories as held by social photo-sharing sites. Assuming that the collection of each photographer’s geotagged photos is a sequence of visited locations, photo-sharing sites are important sources for gathering the location histories of tourists. By following their location sequences, we can find representative and diverse travel routes that link key landmarks. Recommendations are performed by our photographer behavior model, which estimates the probability of a photographer visiting a landmark. We incorporate user preference and present location information into the probabilistic behavior model by combining topic models and Markov models. Based on the photographer behavior model, proposed route recommendation method outputs a set of personalized travel plans that match the user’s preference, present location, spare time and transportation means. We demonstrate the effectiveness of the proposed method using an actual large-scale geotag dataset held by Flickr in terms of the prediction accuracy of travel behavior.     

A Trip Generation Method for Time-Dependent Large-Scale Simulations of Transport and Land-Use

This paper presents an attempt to integrate dynamic traffic models and location choice models in a more consistent way than the four-step planning scheme. Two temporal horizons are considered. On the long run, individuals select the location of their primary activity based on local land-use data and on travel costs. Mode choice is omitted. On the short run, car travelers select the departure time and route on the journey to their the primary activity. On the short run, the travel demand is disaggregated at the individual level. On the long run, users characteristics are aggregated. A methodology is developed that can be used to bootstrap multi-agents simulations of land-use and transport in an efficient way. The emphasis is put on the provision of an operational model. Therefore, the paper focuses on the technical details of the implementation of this methodology and on its applications to large-scale systems. Results are presented on the Zurich area.     

Modeling Business Land Use Equilibrium for Small Firms’ Relocation and Consumers’ Trip in a Transportation Network

This paper studies the integrated models for small and medium-sized enterprises’ distribution and consumers’ trip in an urban network based on the simultaneous equilibrium approach. In this paper, each firm aims at finding some business centers to set up shops to maximize his net profit while each consumer is a traveler who chooses his destination (business center) and travel route according to the minimization of individual net social cost, which is equal to the cost of travel time minus the destination attraction measure. The contribution of this paper can be divided into three parts. Firstly, a new deterministic equilibrium model is developed to capture consumers’ travel choice and firms’ location choice. The relocation cost between any two business centers is explicitly considered. Furthermore, the business center passenger/firm flow capacity constraint is incorporated into the previous model. Mathematically, we prove that this extension will derive the endogenous location transfer market in a business center for firms if the maximum firm flow capacity is reached at equilibrium. Finally, we extend the preceding deterministic models to the stochastic case.     

An Integrated Behavioral Model of Land Use and Transport System: A Hyper-network Equilibrium Approach

The interaction between the land use and transport in the urban context is a relevant issue in policy making. The connection between both systems arises since the former is causal of urban development while the latter is a consequence of it and significant contributor at the same time. One difficulty to unmask such interactions is to understand and determine the global system equilibrium, which is the matter of this paper. The households’ decisions, from their residential location to their travel and route choices, are described as a process of interdependent discrete choices that reflect the long term equilibrium. Consumers are assumed to optimize their combined residence and transport options, which are represented as a set of paths in an extended network that includes the transport system together with fictitious additional links that represents land use and location market. At equilibrium no household is better off by choosing a different option for residential location or by choosing a different set of trips’ destinations and routes. We study several alternative models starting from a simple case with fixed real estate supply and exogenous travel demand, to more complex situations with a real estate market, trip destination choices and variable trip frequencies. The equilibrium is characterized by an equivalent optimization problem which is strictly convex coercive and unconstrained. The optimality conditions for this optimization problem reproduce the transport equilibrium conditions as much as the land use equilibrium conditions. The approach provides a comprehensive characterization of the solution regarding existence and uniqueness, together with an algorithm to obtain the solution with well-defined convergence properties. The model is applicable to real size problems, with heterogeneous population and locations, as well as multiple trip purposes.     

Bus routing in a multi-school system

This paper describes and evaluates a practical computer based method for translating data concerning (1) the location of each school in a multi-school system to be serviced by a bus fleet, (2) the location of each student to be transported to each school, (3) the time period during which students assigned to each school are to be transported, and (4) the available bus facilities into a set of bus routes which specify school-to-school sequencing of each bus and the stop-to-stop route to be followed in traveling to every school. Each route is designed so that bus capacity and student riding time constraints are satisfied while attempting to minimize both total bus travel time (including running empty) and the number of routes required to service all the stops associated with the school. The mathematical models developed were programmed in FORTRAN IV for use on a CDC 6400 computer and were applied to four schools in a Western New York school district. For each school the routing system determined by these mathematical models was better in terms of number of buses and/or travel time than the system in current use. Furthermore, computation time was very reasonable (from 6.9 sec for 596 students, 37 stops to 136.9 sec for 1097 students, 76 stops).     

Neural network model for rapid forecasting of freeway link travel time

Estimation of freeway travel time with reasonable accuracy is essential for successful implementation of an advanced traveler information system (ATIS) for use in an intelligent transportation system (ITS). An ATIS consists of a route guiding system that recommends the most suitable route based on the traveler's requirements using the information gathered from various sources such as loop detectors and probe vehicles. This information can be disseminated through mass media or on on-board satellite-based navigational system. Based on the estimated travel times for various routes, the traveler can make a route choice. In this article, a neural network model is presented for forecasting the freeway link travel time using the counter propagation neural (CPN) network. The performance of the model is compared with a recently reported freeway link travel forecasting model using the backpropagation (BP) neural network algorithm. It is shown that the new model based on the CPN network, and the learning coefficients proposed by Adeli and Park, is nearly two orders of magnitude faster than the BP network. As such, the proposed freeway link travel-forecasting model is particularly suitable for real-time advanced travel information and management systems.     

The Dynamics and Equilibria of Day-to-Day Assignment Models

Traffic network modelling is a field that has developed over a number of decades, largely from the economics of predicting equilibria across route travel choices, in consideration of the congestion levels on those routes. More recently, there has been a growing influence from the psychological and social science fields, leading to a greater interest in understanding behavioural mechanisms that underlie such travel choice decisions. The purpose of the present paper is to describe mathematical models which aim to reflect day-to-day dynamic adjustments in route choice behaviour in response to previous travel experiences. Particularly, the aim is to set these approaches in a common framework with the conventional economic equilibrium models. Starting from the analysis of economic equilibria under perturbations, the presentation moves onto deterministic dynamical system models and stochastic processes. Simple illustrative examples are used to introduce the modelling approaches. It is argued that while such dynamical approaches have appeal, in terms of the range of adaptive behavioural processes that can be incorporated, their estimation may not be trivial. In particular, the obvious solution technique (namely, explicit simulation of the dynamics) can lead to a rather complex problem of interpretation for the model-user, and that more analytical approximation techniques may be a better way forward.     

实时信息下的乘客路径选择行为

智能公交系统伴随着智能交通信息系统的发展而逐渐普及,其目的是向乘客提供各种实时交通信息,以提高出行的便利性和灵活性,最终实现公交出行分担率的提升。针对公交网络的特殊性,提出符合乘客路径选择行为且易于确定的广义路径定义,以成都公交电子站牌信息为背景,设计问卷对乘客路径选择行为及出行意向进行调查。采用定性和定量分析相结合的分析方法,基于随机效用理论,建立包括路径选择方案特性变量和乘客个人社会经济属性特性变量为解释变量的Logit和混合Logit路径选择模型,运用蒙特卡洛模拟和极大似然法完成参数估计。分析结果表明,混合Logit模型能更合理地解释由个体偏好而导致的路径选择行为差异,有助于对复杂公交行为的理解,以便更好地用以指导实践     

顾及主观判断延误的行人路径选择模型

路径选择是人们日常生活中频繁遭遇的现实问题。针对在行人导航系统的辅助下,行人仍然需要通过主观判断识别路径指示信息中的地标和真实地标是否匹配的问题,建立了顾及主观判断延误的行人道路网络模型。通过将主观判断延误时间和路径行程距离引入前景理论中,发展了一种基于前景理论的行人路径选择模型。以中国地质大学(武汉)部分区域为实验对象进行了仿真实验,结果表明所提出的行人路径选择模型给出的最优路径的主观判断延误时间均不超过相同起止点中的最短主观判断延误时间的0.6 s,虽然其给出的行程距离均比最短路径更长,但均不超过16 m。实验结果表明,所提模型符合行人的实际出行需求。     

Comparison of Methods for Path Flow Reassignment for Dynamic User Equilibrium

Models to describe or predict of time-varying traffic flows and travel times on road networks are usually referred to as dynamic traffic assignment (DTA) models or dynamic user equilibrium (DUE) models. The most common form of algorithms for DUE consists of iterating between two components namely dynamic network loading (DNL) and path inflow reassignment or route choice. The DNL components in these algorithms have been investigated in many papers but in comparison the path inflow reassignment component has been relatively neglected. In view of that, we investigate various methods for path inflow reassignment that have been used in the literature. We compare them numerically by embedding them in a DUE algorithm and applying the algorithm to solve DUE problems for various simple network scenarios. We find that the choice of inflow reassignment method makes a huge difference to the speed of convergence of the algorithms and, in particular, find that ??travel time responsive?? reassignment methods converge much faster than the other methods. We also investigate how speed of convergence is affected by the extent of congestion on the network, by higher demand or lower capacity. There appears to be much scope for further improving path inflow reassignment methods.     

A Hybrid Route Choice Model for Dynamic Traffic Assignment

Network user equilibrium or user optimum is an ideal state that can hardly be achieved in real traffic. More often than not, every day traffic tends to be in disequilibrium rather than equilibrium, thanks to uncertainties in demand and supply of the network. In this paper we propose a hybrid route choice model for studying non-equilibrium traffic. It combines pre-trip route choice and en-route route choice to solve dynamic traffic assignment (DTA) in large-scale networks. Travelers are divided into two groups, habitual travelers and adaptive travelers. Habitual travelers strictly follow their pre-trip routes which can be generated in the way that major links, such as freeways or major arterial streets, are favored over minor links, while taking into account historical traffic information. Adaptive travelers are responsive to real-time information and willing to explore new routes from time to time. We apply the hybrid route choice model in a synthetic medium-scale network and a large-scale real network to assess its effect on the flow patterns and network performances, and compare them with those obtained from Predictive User Equilibrium (PUE) DTA. The results show that PUE-DTA usually produces considerably less congestion and less frequent queue spillback than the hybrid route choice model. The ratio between habitual and adaptive travelers is crucial in determining realistic flow and queuing patterns. Consistent with previous studies, we found that, in non-PUE DTA, supplying a medium sized group (usually less than 50%) of travelers real-time information is more beneficial to network performance than supplying the majority of travelers with real-time information. Finally, some suggestions are given on how to calibrate the hybrid route choice model in practice to produce realistic results.     

Popular route planning with travel cost estimation from trajectories

With the increasing number of GPS-equipped vehicles,more and more trajectories are generated continuously,based on which some urban applications become feasible,such as route planning.In general,popular route that has been travelled frequently is a good choice,especially for people who are not familiar with the road networks.Moreover,accurate estimation of the travel cost(such as travel time,travel fee and fuel consumption)will benefit a wellscheduled trip plan.In this paper,we address this issue by finding the popular route with travel cost estimation.To this end,we design a system consists of three main components.First,we propose a novel structure,called popular traverse graph where each node is a popular location and each edge is a popular route between locations,to summarize historical trajectories without road network information.Second,we propose a self-adaptive method to model the travel cost on each popular route at different time interval,so that each time interval has a stable travel cost.Finally,based on the graph,given a query consists of source,destination and leaving time,we devise an efficient route planning algorithmwhich considers optimal route concatenation to search the popular route from source to destination at the leaving time with accurate travel cost estimation.Moreover,we conduct comprehensive experiments and implement our system by a mobile App,the results show that our method is both effective and efficient.     

改进的蚁群算法在动态路径诱导中的应用研究

针对基本蚁群算法收敛速度慢和易陷入局部最优的缺点,在对信息素和启发信息进行标准化以消除量纲和取值范围影响的基础上,提出带方向的信息素更新和混沌选择策略来改进蚁群算法。将路网节点间的相对位置信息引入信息素更新,以加快搜索速度;使用混沌扰动改进选择策略,以避免出现早熟停滞现象。并将其用于城市交通动态路径诱导的研究中,以重庆市渝中半岛的路网为实例计算以最短行程时间为目标的最优路径,结果表明该算法是有效、可行的,比基本蚁群算法具有更好的全局搜索能力。     

Paradigms to Deploy a Behavior-Consistent Approach for Information-Based Real-Time Traffic Routing

A behavior-consistent information-based network control approach determines real-time traffic routing strategies by explicitly accounting for drivers’ likely response to the controller-recommended routes while generating these strategies. This paper proposes paradigms to deploy a behavior-consistent approach developed by the authors (Paz and Peeta 2007). These paradigms seek to enhance deployment effectiveness by analyzing the effects of alternative controller objectives and driver-preferred route sets used to recommend routes. Experiments are conducted using a test network. They analyze: (1) the performance of the behavior-consistent approach under commonly-used controller objectives, (2) the deployment flexibility enabled by increasing the number of driver-preferred routes considered by the controller for routing, and (3) the effects of augmenting the driver-preferred route choice set through various paradigms. The results suggest that the behavior-consistent approach can perform better than standard dynamic traffic assignment models while directing the system towards the desired state. They also illustrate the effectiveness of considering more driver-preferred routes in developing the information strategies. Further, they suggest that driver-preferred route choice set augmentation and the associated route types can have differential impacts on performance. Also, performance is influenced by trade-offs between the number of driver-preferred routes considered by the controller for routing and the quality of routes relative to the controller objective. The results suggest that higher compliance rates may not translate to better performance and question the justification of user equilibrium solutions for route guidance on the ground that a system optimal strategy is not behaviorally sustainable.     

Route finding by using knowledge about the road network

Traveling is a part of every person's day-to-day life. With the massive and complicated road network of a modern city or country, finding a good route to travel from one place to another is not a simple task. In network theory, this is the shortest path problem. Shortest-path algorithms are often used to solve this problem. However, these algorithms are wasteful in terms of computation when applied to the route-finding task. They may also produce routes that are not suitable for human users. In practice, knowledge about the road network can often be used to reduce the time and space required in computation, and to produce human-oriented solutions. In this project, we have integrated knowledge-based technique and algorithmic method to solve the problem. This integrated approach substantially reduces the computation time and space required for route finding. Within the approach we present three alternative designs, which may be suitable for different situations     

A User-Centered Location Model

This paper discusses the user-centered location model used in comMotion. In this context, the location model refers to a set of learned places (destinations), which coincide to a latitude and a longitude, that the user has categorized. It also includes knowledge of the routes between the destinations and the time it takes to travel them. The model is based on user experience, i.e. his patterns of mobility, so no two models are the same. We also discuss the pattern recognition models implemented for route learning, route prediction and estimation of time to arrival. Correspondence to: Ms N. Marmasse, MIT Media Laboratory, 20 Ames Street, Cambridge, MA 02139, USA. Email: nmarmas@media.mit.edu     

Allocating time and location information to activity–travel patterns through reinforcement learning

The Reinforcement Machine Learning technique presented in this paper simulates time and location information for a given sequence of activities and transport modes. The main contributions to the current state-of-the art are the allocation of location information in the simulation of activity–travel patterns, the non-restriction to a given number of activities and the incorporation of realistic travel times. Furthermore, the time and location allocation problem were treated and integrated simultaneously, which means that the respondents’ reward is not only maximized in terms of minimum travel duration, but also simultaneously in terms of optimal time allocation.