An integrated Bayesian approach for passenger flow assignment in metro networks

This paper proposes an integrated Bayesian statistical inference framework to characterize passenger flow assignment model in a complex metro network. In doing so, we combine network cost attribute estimation and passenger route choice modeling using Bayesian inference. We build the posterior density by taking the likelihood of observing passenger travel times provided by smart card data and our prior knowledge about the studied metro network. Given the high-dimensional nature of parameters in this framework, we apply the variable-at-a-time Metropolis sampling algorithm to estimate the mean and Bayesian confidence interval for each parameter in turn. As a numerical example, this integrated approach is applied on the metro network in Singapore. Our result shows that link travel time exhibits a considerable coefficient of variation about 0.17, suggesting that travel time reliability is of high importance to metro operation. The estimation of route choice parameters conforms with previous survey-based studies, showing that the disutility of transfer time is about twice of that of in-vehicle travel time in Singapore metro system.     

Implicit choice set generation in discrete choice models: Application to household auto ownership decisions

Latent choice set models that account for probabilistic consideration of choice alternatives during decision making have long existed. The Manski model that assumes a two-stage representation of decision making has served as the standard workhorse model for discrete choice modeling with latent choice sets. However, estimation of the Manski model is not always feasible because evaluation of the likelihood function in the Manski model requires enumeration of all possible choice sets leading to explosion for moderate and large choice sets. In this study, we propose a new group of implicit choice set generation models that can approximate the Manski model while retaining linear complexity with respect to the choice set size. We examined the performance of the models proposed in this study using synthetic data. The simulation results indicate that the approximations proposed in this study perform considerably well in terms of replicating the Manski model parameters. We subsequently used these implicit choice set models to understand latent choice set considerations in household auto ownership decisions of resident population in the Southern California region. The empirical results confirm our hypothesis that certain segments of households may only consider a subset of auto ownership levels while making decisions regarding the number of cars to own. The results not only underscore the importance of using latent choice models for modeling household auto ownership decisions but also demonstrate the applicability of the approximations proposed in this study to estimate these latent choice set models.     

Modelling bicycle use intention: the role of perceptions

Users’ perceptions are identified as key elements to understand bicycle use, whose election cannot be explained with usual mobility variables and socio-economic characteristics. A hybrid model is proposed to model the intention of bicycle use; it combines a structural equations model that captures intentions and a choice model. The framework is applied to a case of a university campus in Madrid that is studying a new internal bike system. Results show that four latent variables (convenience, pro-bike, physical determinants and external restrictions) help explaining intention to use bike, representing a number of factors that are linked to individual perceptions.     

Multi-level forces and differential effects affecting birth cohorts that stimulate mobility change*

This paper presents a dynamic model at three levels to understand changing mobility trends at the population level. A multi-level framework is proposed that enables existing research and analysis to be considered in a more holistic sense. This framework assists in identifying predictions and transition pathways for different birth cohorts, particularly as they reach older age. This has the aim of bringing about a greater understanding of the socio-demographic influence on mobility trends, with a focus on the cultural transitions that affect birth cohorts differently in terms of their travel behaviour. The framework presented here captures the multi-level forces and structural effects that impact mobility. The paper examines how these forces and effects interact at different levels to influence the changing mobility of birth cohorts at different points in time. Examining the simultaneous operation of these levels is of conceptual importance to assist in the interpretation of mobility trends, as well as understanding future mobility implications, of future generations.     

What Curriculum for Mobility and Transport Studies? A Critical Exploration

To understand the complex meanings of mobility and to engage in transport planning and management processes, a variety of disciplines, skills, and tools are potentially useful. Universities have a limited amount of time and resources to train future professionals though. This poses a problem: where should the teaching priorities be? By means of a web-survey, this study has asked academics based at a number of universities what the disciplines, skills, and tools that — according to their personal viewpoints — are the most relevant for practitioners in the mobility and transport sector. The respondents generally support curricula that facilitate a holistic, non-specialised, understanding of mobility and transport issues.     

How,when and why integrated choice and latent variable models are latently useful

Integrated Choice and Latent Variable (ICLV) models are an increasingly popular extension to discrete choice models that attempt explicitly to model the cognitive process underlying the formation of any choice. This study was born from the discovery that an ICLV model can in many cases be reduced to a choice model without latent variables that fits the choice data at least as well as the original ICLV model from which it was obtained. The failure of past studies to recognize this fact raised concerns about other benefits that have been claimed with regards to the framework. With the objective of addressing these concerns, this study undertakes a systematic comparison between the ICLV model and an appropriately specified reduced form choice model. We derive analytical proofs regarding the benefits of the framework and use synthetic datasets to corroborate any conclusions drawn from the analytical proofs. We find that the ICLV model can under certain conditions lead to an improvement in the analyst's ability to predict outcomes to the choice data, allow for the identification of structural relationships between observable and latent variables, correct for bias arising from omitted variables and measurement error, reduce the variance of parameter estimates, and abet practice and policy, all in ways that would not be possible using the reduced form choice model. We synthesize these findings into a general process of evaluation that can be used to assess what gains, if any, might be had from developing an ICLV model in a particular empirical context.     

A latent variable representation of count data models to accommodate spatial and temporal dependence: Application to predicting crash frequency at intersections

This paper proposes a reformulation of count models as a special case of generalized ordered-response models in which a single latent continuous variable is partitioned into mutually exclusive intervals. Using this equivalent latent variable-based generalized ordered response framework for count data models, we are then able to gainfully and efficiently introduce temporal and spatial dependencies through the latent continuous variables. Our formulation also allows handling excess zeros in correlated count data, a phenomenon that is commonly found in practice. A composite marginal likelihood inference approach is used to estimate model parameters. The modeling framework is applied to predict crash frequency at urban intersections in Arlington, Texas. The sample is drawn from the Texas Department of Transportation (TxDOT) crash incident files between 2003 and 2009, resulting in 1190 intersection-year observations. The results reveal the presence of intersection-specific time-invariant unobserved components influencing crash propensity and a spatial lag structure to characterize spatial dependence. Roadway configuration, approach roadway functional types, traffic control type, total daily entering traffic volumes and the split of volumes between approaches are all important variables in determining crash frequency at intersections.     

Analyzing year-to-year changes in public transport passenger behaviour using smart card data

In recent years, there has been increased interest in using completely anonymized data from smart card collection systems to better understand the behavioural habits of public transport passengers. Such an understanding can benefit urban transport planners as well as urban modelling by providing simulation models with realistic mobility patterns of transit networks. In particular, the study of temporal activities has elicited substantial interest. In this regard, a number of methods have been developed in the literature for this type of analysis, most using clustering approaches. This paper presents a two-level generative model that applies the Gaussian mixture model to regroup passengers based on their temporal habits in their public transportation usage. The strength of the proposed methodology is that it can model a continuous representation of time instead of having to employ discrete time bins. For each cluster, the approach provides typical temporal patterns that enable easy interpretation. The experiments are performed on five years of data collected by the Société de transport de l’Outaouais. The results demonstrate the efficiency of the proposed approach in identifying a reduced set of passenger clusters linked to their fare types. A five-year longitudinal analysis also shows the relative stability of public transport usage.     

A study of human mobility behavior dynamics: A perspective of a single vehicle with taxi

In this paper, we first research on the distance distribution of human mobility with single vehicle based on the driving data from a taxi company in South China. Different from conventional exponential distribution, we discover the mobility distance with taxi follows power-law distribution. Further, we proposed a model which may explain the mechanism for the power-law distribution: mobility distance is constrained by time and fare. Specifically, the relationship between fare and mobility distance follows piecewise function, and responds to individual sensitivity; the relationship between time and mobility distance follows significant logarithmic relationship. These two factors, especially the logarithmic relationship between time and mobility distance, may contribute to a power-law distribution instead of an exponential one. Finally, with a simulation model, we verify the significant power-law distribution of human mobility behavioral distance with a single vehicle, by supplementing factors of waiting time and fare.     

Analyzing commuter train user behavior: a decision framework for access mode and station choice

The purpose of the current research effort is to develop a framework for a better understanding of commuter train users’ access mode and station choice behavior. Typically, access mode and station choice for commuter train users is modeled as a hierarchical choice with access mode being considered as the first choice in the sequence. The current study proposes a latent segmentation based approach to relax the hierarchy. In particular, this innovative approach simultaneously considers two segments of station and access mode choice behavior: Segment 1—station first and access mode second and Segment 2—access mode first and station second. The allocation to the two segments is achieved through a latent segmentation approach that determines the probability of assigning the individual to either of these segments as a function of socio-demographic variables, level of service (LOS) parameters, trip characteristics, land-use and built environment factors, and station characteristics. The proposed latent segment model is estimated using data from an on-board survey conducted by the Agence Métropolitaine de Transport for commuter train users in Montreal region. The model is employed to investigate the role of socio-demographic variables, LOS parameters, trip characteristics, land-use and built environment factors, and station characteristics on commuter train user behavior. The results indicate that as the distance from the station by active forms of transportation increases, individuals are more likely to select a station first. Young persons, females, car owners, and individuals leaving before 7:30 a.m. have an increased propensity to drive to the commuter train station. The station model indicates that travel time has a significant negative impact on station choice, whereas, presence of parking and increased train frequency encourages use of the stations.     

A generic data assimilation framework for vehicle trajectory reconstruction on signalized urban arterials using particle filters

With trajectory data, a complete microscopic and macroscopic picture of traffic flow operations can be obtained. However, trajectory data are difficult to observe over large spatiotemporal regions—particularly in urban contexts—due to practical, technical and financial constraints. The next best thing is to estimate plausible trajectories from whatever data are available. This paper presents a generic data assimilation framework to reconstruct such plausible trajectories on signalized urban arterials using microscopic traffic flow models and data from loops (individual vehicle passages and thus vehicle counts); traffic control data; and (sparse) travel time measurements from whatever source available. The key problem we address is that loops suffer from miss- and over-counts, which result in unbounded errors in vehicle accumulations, rendering trajectory reconstruction highly problematic. Our framework solves this problem in two ways. First, we correct the systematic error in vehicle accumulation by fusing the counts with sparsely available travel times. Second, the proposed framework uses particle filtering and an innovative hierarchical resampling scheme, which effectively integrates over the remaining error distribution, resulting in plausible trajectories. The proposed data assimilation framework is tested and validated using simulated data. Experiments and an extensive sensitivity analysis show that the proposed method is robust to errors both in the model and in the measurements, and provides good estimations for vehicle accumulation and vehicle trajectories with moderate sensor quality. The framework does not impose restrictions on the type of microscopic models used and can be naturally extended to include and estimate additional trajectory attributes such as destination and path, given data are available for assimilation.     

How habit moderates the commute mode decision process: integration of the theory of planned behavior and latent class choice model

Transportation - This study attempts to develop a comprehensive framework by integrating the theory of planned behavior (TPB) and latent class choice model, with aim to understanding how mode-use...     

Inferring origin-destination pairs and utility-based travel preferences of shared mobility system users in a multi-modal environment

This paper presents a methodological framework to identify population-wide traveler type distribution and simultaneously infer individual travelers’ Origin-Destination (OD) pairs, based on the individual records of a shared mobility (bike) system use in a multimodal travel environment. Given the information about the travelers’ outbound and inbound bike stations under varied price settings, the developed Selective Set Expectation Maximization (SSEM) algorithm infers an underlying distribution of travelers over the given traveler “types,” or “classes,” treating each traveler’s OD pair as a latent variable; the inferred most likely traveler type for each traveler then informs their most likely OD pair. The experimental results based on simulated data demonstrate high SSEM learning accuracy both on the aggregate and dissagregate levels.     

Who you are is how you travel: A framework for transportation mode detection using individual and environmental characteristics

With the increasing prevalence of geo-enabled mobile phone applications, researchers can collect mobility data at a relatively high spatial and temporal resolution. Such data, however, lack semantic information such as the interaction of individuals with the transportation modes available. On the other hand, traditional mobility surveys provide detailed snapshots of the relation between socio-demographic characteristics and choice of transportation modes. Transportation mode detection is currently approached using features such as speed, acceleration and direction either on their own or in combination with GIS data. Combining such information with socio-demographic characteristics of travellers has the potential of offering a richer modelling framework that could facilitate better transportation mode detection using variables such as age and disability. In this paper, we explore the possibility to include both elements of the environment and individual characteristics of travellers in the task of transportation mode detection. Using dynamic Bayesian Networks, we model the transition matrix to account for such auxiliary data by using an informative Dirichlet prior constructed using data from traditional mobility surveys. Results have shown that it is possible to achieve comparable accuracy with the most widely used classification algorithms while having a rich modelling framework, even in the case of sparse mobility data.     

A complex network perspective for characterizing urban travel demand patterns: graph theoretical analysis of large-scale origin–destination demand networks

Urban travel demand, consisting of thousands or millions of origin–destination trips, can be viewed as a large-scale weighted directed graph. The paper applies a complex network-motivated approach to understand and characterize urban travel demand patterns through analysis of statistical properties of origin–destination demand networks. We compare selected network characteristics of travel demand patterns in two cities, presenting a comparative network-theoretic analysis of Chicago and Melbourne. The proposed approach develops an interdisciplinary and quantitative framework to understand mobility characteristics in urban areas. The paper explores statistical properties of the complex weighted network of urban trips of the selected cities. We show that travel demand networks exhibit similar properties despite their differences in topography and urban structure. Results provide a quantitative characterization of the network structure of origin–destination demand in cities, suggesting that the underlying dynamical processes in travel demand networks are similar and evolved by the distribution of activities and interaction between places in cities.     

Large-scale transit itinerary planning under uncertainty

In this paper, we study the transit itinerary planning problem with incorporation of randomness that arises in transit vehicle arrival/departure and passenger transfer. We investigate two approaches to address the uncertainty: a minmax robust approach and an expectation-based probabilistic approach. We adapt a two-phase framework to mitigate computational challenges in large-scale planning problems. In phase I, we compute candidate route connections offline and store them into a database. Although expensive computation is required in phase I, it is typically performed only once over a period of time (e.g., half a year). Phase II takes place whenever a request is received, for which we query candidate route connections from the database, build a stochastic shortest-path model based on either approach listed above, and solve the model in real time. With phase I, computational requirement in phase II is substantially reduced so as to ensure real-time itinerary planning. To demonstrate the practical feasibility of our two-phase approach, we conduct extensive case studies and sensitivity analyses based on a large real-world transit network.     

Measurement of intention to travel: Considering the effect of telecommunications on trips

Mobility, one of the key concepts to evaluate the effect of a transportation policy such as TDM and mobility management as well as to analyze the problems such as social exclusion, must be measured by how much of an intention to make a trip can be realized, not merely by how many trips are available. The advent of new communication tools such as the Internet and mobile phones has allowed one to accomplish certain tasks that previously required a trip. This new situation has brought up a discussion over the necessity to incorporate telecommunications as an aspect of mobility. Aware of such discussions, we analyzed the relationship between the number of trips and telecommunications based on the data we collected on trips, telecommunications, and activities, and found some significant correlations. Our study which used an ordered regression model found several significant relationships between the individual attributes and the number of trips/telecommunications. We formulated a model which assumes the latent factors among the trips and telecommunications. In addition, we found that the latent factors construable as intentions to trips and telecommunications could be measured better by e-mails than by trips. These results indicate that measuring mobility requires the inclusion of information about telecommunications.     

Mobility evaluation for urban public transportation

Summary

This paper has reported on a study of relative opportunity—not absolute opportunity. Minimum absolute standards for mobility or accessibility are difficult to justify. Some additional study into the development and application of absolute mobility standards may be warranted.

The application of the mobility evaluation model has primarily focused upon a corridor line‐haul system. Conclusions suggest that such a system will not markedly improve existing transit mobility levels in either the peak hour or the off‐peak. The experimental work has verified this conclusion, and more importantly, it has detailed quantitatively the exact levels and spatial distribution of mobility improvements. However, this study does not include a comprehensive analysis of all methods of mobility enhancement, nor does it undertake a comparison of alternative means of mobility improvement. Certainly other methods to improve access to opportunities should be explored before policy considerations are finalized. These methods include other transit solutions, land use alternatives, socio‐economic policies, and other‐mode transportation alternatives. The accessibility technique and mobility indices approach appears to have general applicability in the analysis of optimal strategies for system evaluation.

Of interest is an examination of alternative feeder transit systems to the corridor line. Additional research with the model might point out the maximum mobility effects expected through improved collector service in the suburbs, with corridor line‐haul to the CBD.

The indices are also readily available for a comparison of mobility patterns for different urban areas. Application of the program to transit and socio‐economic data for a set of cities would yield an indication of the relative mobility levels provided. Such data might be considered as an evaluation criterion for future transit funding by federal officials.

In addition, the model is currently being considered by UMTA as a tool to aid in the evaluation of the equitable distribution of transit system benefits as defined in Title VI of the Civil Rights Act of 1964.²⁵ The mobility output would serve as an indicator of the levels‐of‐service provided to certain disadvantaged urban groups. For this application the computer model is being altered to achieve compatability with the Transportation Planning System (UTPS) computer model package developed by UMTA.     

Forecasting travel behavior using Markov Chains-based approaches

Recent advances in agent-based micro-simulation modeling have further highlighted the importance of a thorough full synthetic population procedure for guaranteeing the correct characterization of real-world populations and underlying travel demands. In this regard, we propose an integrated approach including Markov Chain Monte Carlo (MCMC) simulation and profiling-based methods to capture the behavioral complexity and the great heterogeneity of agents of the true population through representative micro-samples. The population synthesis method is capable of building the joint distribution of a given population with its corresponding marginal distributions using either full or partial conditional probabilities or both of them simultaneously. In particular, the estimation of socio-demographic or transport-related variables and the characterization of daily activity-travel patterns are included within the framework. The fully probabilistic structure based on Markov Chains characterizing this framework makes it innovative compared to standard activity-based models. Moreover, data stemming from the 2010 Belgian Household Daily Travel Survey (BELDAM) are used to calibrate the modeling framework. We illustrate that this framework effectively captures the behavioral heterogeneity of travelers. Furthermore, we demonstrate that the proposed framework is adequately adapted to meeting the demand for large-scale micro-simulation scenarios of transportation and urban systems.     

A Bayesian network approach for population synthesis

Agent-based micro-simulation models require a complete list of agents with detailed demographic/socioeconomic information for the purpose of behavior modeling and simulation. This paper introduces a new alternative for population synthesis based on Bayesian networks. A Bayesian network is a graphical representation of a joint probability distribution, encoding probabilistic relationships among a set of variables in an efficient way. Similar to the previously developed probabilistic approach, in this paper, we consider the population synthesis problem to be the inference of a joint probability distribution. In this sense, the Bayesian network model becomes an efficient tool that allows us to compactly represent/reproduce the structure of the population system and preserve privacy and confidentiality in the meanwhile. We demonstrate and assess the performance of this approach in generating synthetic population for Singapore, by using the Household Interview Travel Survey (HITS) data as the known test population. Our results show that the introduced Bayesian network approach is powerful in characterizing the underlying joint distribution, and meanwhile the overfitting of data can be avoided as much as possible.