Travel Demand Model Research Articles

Decoding Behavioural Norms in School Mobility: A Structural Equation Modelling Analysis

Travel demand modelling for school travel, remains a subject of limited research. The exploitation of factors influencing parents in school mode choice and the understanding of the significance parents attribute to these factors is important, contributing to transport planning and leading to a strategic direction with an ultimate scope to improve the school transportation system and promote the use of alternative transport modes for upgrading the living environment and quality of life in general. The current paper examines the development of a Structural Equation Model (SEM) describing the interrelationships between the factors influencing parents in the decision-making process and the final mode choice. For that, a questionnaire survey is conducted for parents of children aged six to eighteen years old. The collected data are analysed through Exploratory and Confirmatory Factor Analysis. Following, an SEM is developed examining the proposed authors' conceptual model, basic hypotheses of school travel choice, and direct and indirect correlations of factors composing parental behaviour.

Quality Assessment of OpenStreetMap’s Points of Interest with Large-Scale Real Data

OpenStreetMap (OSM) data are geographical data that are easy and open to access and therefore used for a large set of applications including travel demand modeling. However, often there is a limited awareness about the shortcomings of volunteered geographic information data, such as OSM. One important issue for the application in travel demand modeling is the completeness of OSM elements, particularly points of interest (POI), since it directly influences the predictions of trip distributions. This might cause unreliable model sensitivities and end up in wrong predictions leading to expensive misinterpretations of the effects of policy measures. Because of a lack of large-scale real-world data, a detailed assessment of the quality of POI from OSM has not been done yet. Therefore, in this work, we assess the quality of POI from OSM for use within travel demand models using surveyed real-world data from 49 areas in Germany. We perform a descriptive and a model-based analysis using spatial, demographic, and intrinsic indicators for two common trip purpose categories used in travel demand modeling. We show that the completeness of POI data in OSM depends on the category of POI. We further show that intrinsic indicators and indicators calculated based on data from other sources (e.g., land use or census data) are able to detect quality deficiencies of OSM data.

Microsimulation of activity generation, activity scheduling and shared travel choices within an activity-based travel demand modelling system

This study presents the microsimulation of activity generation, activity scheduling and shared travel choice processes within an activity-based shorter-term decisions simulator (SDS). Activity generation simulates individuals’ daily activities in an orderly fashion utilizing a Markov Chain Monte Carlo modelling technique within the SDS model. Activity scheduling is simulated as a process of activity agenda formation, destination location choice and shared travel choice decisions utilizing heuristics and econometric modelling (e.g. mixed logit) approaches. The proposed SDS model addresses multi-way interactions among different activity and travel decision components. It also accounts for individuals’ social interactions within the modelling framework. The model is implemented within C#.NET platform for Halifax, Canada from 2006 to 2036 on each year interval. The performance of the model is measured based on absolute percentage error (APE) values for the year 2011, and microsimulation results are validated with the 2016 Canadian Census information. The simulation results suggest that higher density of activity destinations is predicted within approximately 10 km distances from home locations in 2006. Interestingly, with time, the densities become more skewed to the left of 10 km. The highest percentage of non-shared travel (32%) is predicted for a travel length of less than 5 km in 2036, which decreases with distance. In contrast, the proportions of shared travel choice alternatives are predicted to increase as travel length increases. This paper offers critical insights on spatio-temporal evolution of activity and travel decisions, which will be useful for integrated transportation and land use policy making.

Validation of a multi-modal transit route choice model using smartcard data

Validation of travel demand models, although recognised as important, is seldom undertaken. This study adds to the scarce literature in this field by undertaking an external validation of a multi-modal transit route choice model. The model was estimated using smart card data for the urban transit network of Amsterdam before the introduction of a new metro line and is used to predict changes in travel behaviour after the network change. To validate, the model was checked for changes in estimated parameters between the two time periods, and predictive ability was evaluated at different aggregation levels. Although most model parameters were found to be unstable between the two contexts, the predictive performance at all levels was similar to the locally estimated model. Moreover, individual choices and transit mode-share predictions were found to be close to the observed ones. The errors were relatively larger for the link and route-level predictions, some of which could be attributed to the assumptions made regarding consideration choice set given as input to the model. On comparing alternative model specifications, using generic instead of mode-specific travel attributes lead to a strong degradation in predictive performance. Conversely, a model incorporating overlap between routes, with a better model fit in the base period, did not offer a clear improvement in prediction performance. The study highlights the need to validate transit route choice models before using them for deriving policy recommendations, especially in this data-rich age in which it can often be undertaken at a relatively low additional cost.

Are Telecommuters’ and Non-Telecommuters’ Daily Time-Use Behaviors Different? An Episode-Level Model for Non-Mandatory Activities

Telecommuters (individuals working from home), and non-telecommuters (individuals working at the workplace) might have significantly different daily time-use patterns. For example, telecommuters might be able to engage in more episodes of non-mandatory activities such as recreation and pick-up/drop-off of children, as many telecommuters have a relatively flexible work arrangement in relation to their work location and schedule. This study aims to explore the non-mandatory activity engagement and duration decisions of these two worker profiles at a disaggregated-episode level. To do so, a multiple discrete continuous extreme value with ordered preferences (MDCEV-OP) model is adopted, using the 2018 travel survey data from the Central Okanagan region of British Columbia, Canada. This model accounts for multiple occurrences of each activity type (i.e., episodes) along with their corresponding durations. In doing so, the model adopts a logical ordering ensuring that the jth episode is not predicted without the occurrence of the ( j–1)th episode of an activity type. The model results reveal that telecommuters are more likely to engage in higher episodes of non-mandatory activities, compared with non-telecommuters. For instance, female teleworkers are found to be more likely to participate in higher episodes of activities associated with household-related responsibilities such as shopping. In contrast, female non-telecommuters are associated with participating in more episodes of personal business-related activities. The findings of this study provide important behavioral insights into the activity-time-use patterns of telecommuters and non-telecommuters, which can be utilized to develop more effective and equitable travel demand management plans, policies, and models.

On modeling future workplace location decisions: An analysis of Texas employees

In this paper, we examine work place location (WPL) preferences of workers in an unpredictable and evolving future by investigating how workers would prefer to allocate their monthly working days among the three WPL alternatives of working from home, from the work office, and from a variable third WPL. In contrast to earlier studies that typically focus on telework as a binary of whether an individual is a teleworker or not, we focus our attention on workers’ preferences for specific combinations of all three WPLs over a period of a month (including, but not limited to, only selecting one WPL for all days of the month). In our analysis, we employ the multiple discrete–continuous extreme value (MDCEV) model, using a 2022 stated preference survey of future work preferences of employees residing in the state of Texas. The results indicate that single young women with very young children, those with long commutes and “intolerable” traffic congestion to the work office, individuals with a private study in their homes, self-employed workers, and those in non-essential service occupations have the highest preference for working from home. On the other hand, older men, individuals from low income households, those residing in rural areas, and workers in essential service occupations have the highest preference for the work office. And, for the third WPL, young non-single women with very young children, individuals from low income households, part-time employees, and those in professional, managerial or finance occupations have the highest predisposition. These results should provide valuable insights to urban planners, homebuilders, employers, travel demand modelers, and a whole host of other businesses to achieve specific desired end states. From a data collection standpoint, our study underscores the need to collect detailed information about work patterns in future activity-travel surveys.

A Survey on Traffic Modeling and Mitigation Techniques

Abstract—The growing road traffic congestion in India is becoming more prevalent nowadays, the worst hit being the metropolitan and developed cities. Its ever-increasing nature poses a threat to development and requires quick action to help ease the troubles faced by people on a day-to-day basis. Modern technology offers a great aid to these efforts by providing mechanisms to model the travel patterns of a region and provide quick congestion relief by introducing adaptive traffic signals that operate based on the current traffic density of the road. In this paper, we present a survey of the most popular techniques involving travel demand modeling and adaptive signal control and compare the various methods employed by researchers to tackle both of these issues separately. The survey focuses on analyzing these methods from the perspective of implementation in an Indian scenario and offering a theoretical solution that involves the use of both these techniques to ease the problem of traffic congestion. Index Terms—road traffic congestion mitigation, adaptive traf- fic signal, travel demand modeling,

Impact of Life Events on Incremental Travel Behavior Change

Travel demand models regenerate the travel behavior of persons or households from scratch at every model run. However, the literature suggests that travel behavior remains relatively stable over time. Change in travel behavior is triggered by life events such as change in employment, household relocation, or birth of a child. The inability of existing travel demand models to represent habitual travel behavior and change in travel behavior of a person/household becuase of life events tends to exaggerate policy sensitivity and result in longer model run times to recreate travel behavior for every agent. In this study, we examined the travel behavior of persons between two consecutive years using a mobility panel survey from Germany. The travel behavior of persons with and without a life event is compared econometrically. Here, the travel behavior is measured as the number of weekly trips by activity type and mode and the impacts of six types of life events are studied. The results show that life events affect travel behavior, but the degree of impact varies by the type of life event, the trip purpose, and the mode. In some cases the impact is found to be negligible, but for many other cases the impact is profound. Moreover, general trends (not affected by life event) in travel behavior are also found. It is concluded that such dynamics in travel behavior should be represented by travel demand models for more sensible policy testing and computationally efficient travel demand models.

On the use of common random numbers in activity-based travel demand modeling for scenario comparison

ABSTRACT Activity-based travel demand models provide a high level of detail when modeling complex travel behavior. Since stochastic simulation is used, however, this high level may induce large random fluctuations in the output, necessitating many model reruns to produce reliable output. This may become prohibitive in terms of computation time when comparing travel behavior between multiple scenarios, in which case each scenario requires its own simulation. To alleviate this issue, we study the use of common random numbers, which is a technique that reuses the same random numbers for choices made by travelers between scenarios. This ensures that any observed difference in output across scenarios cannot be attributed to mutual differences in drawn random numbers, eliminating an important source of random fluctuation. We demonstrate by a numerical study that common random numbers can greatly reduce the number of runs needed, and thus also the required computation time, to obtain reliable output.

Urban transport emissions under current and alternative mitigation policy scenarios for the Mumbai Metropolitan region

This paper first estimates the emission inventory for Mumbai Metropolitan Region's (MMR) urban transport under business-as-usual (BAU) scenario. The inventory includes five major pollutants, viz. CO2, CO, NOx, PM, and HC. We used a bottom-up approach based on a traditional four-step travel demand model for the base year (2005) and horizon years (2031 & 2050) to obtain vehicle kilometer traveled (VKT) and vehicular emission factors adopted from the existing literature. The paper then proposes five emission mitigation scenarios for horizon years, including improvement of public transit infrastructure, increase in vehicle operating costs and inclusion of electric vehicles (EVs), to evaluate their impact on the urban transport of MMR. A combination of multiple policies- including the combined effect of land use, increase in vehicle operating cost and transit infrastructure- will reduce private vehicle mode share, VKT, and CO2 emission by ∼21%, ∼19%, and ∼4% compared to the BAU scenario in 2050.

A novel modelling approach of integrated taxi and transit mode and route choice using city-scale emerging mobility data

The modelling of mode and route choices of public transport passengers is an essential component of travel demand modelling and transportation planning. Traditionally, choice models are trained using data from revealed and stated preference surveys which are not only cost and time intensive but also suffer from bias due to limitations in sample size. In this article, we present a novel approach utilizing a combination of emerging data sources for non-private modes, namely public transit smart card data, taxi GPS trajectory data, and taxi trips transaction data in order to calibrate an integrated taxi and transit mode and route choice model. We apply our approach for a case study of the taxi and public transport system of Singapore using data sets obtained from the local transport authorities. We solve the first mile and last mile data gaps by overlaying the individual transport nodes against geospatial land use data in order to identify their actual origin and destination points of each journey. To model the behavioural inter-dependencies between the choice of taxi and transit options, we tested a two-level nested logit model and a cross-nested logit model. Along with the commonly included exploratory variables such as in-vehicle travel time, transfer time and number of transfers, our model also incorporates differences in travel cost and separate mode specific constants for peak and off-peak periods. Generic and mode-specific in-vehicle travel time and cost coefficients are tested in the utility functions for the transit and taxi alternatives. Willingness to pay estimates are calculated and compared against similar estimates from Singapore. We also present an application of the model by predicting the mode split between taxi and transit under selected transit and taxi fare change scenarios. Our modelling methodology is highly generalizable and can be applied to other cities with similar data availability.

Activity Schedule Modeling Using Machine Learning

A novel data-driven approach for activity schedule modeling is presented in this paper. The paper’s contribution is twofold. First, the activity schedule is modeled as a time series to facilitate simultaneous prediction of activity participation, start times, and duration. Simultaneous prediction helps avoid assuming a predefined decision structure and allows all possible interdependencies among these choice facets to be modeled. The time series representation also ensures time budget constraints are automatically satisfied. Second, a machine learning tool called long short-term memory (LSTM) network is used to model the time series. The LSTM’s ability to model long-term dependencies ensures that activity patterns are generated considering the influence of distant and recent past. A bidirectional LSTM is used to capture the effect of (planned) future activities on the present activity participation. The model derives all the relations from the data without requiring assumptions by the modeler on the decision-making behavior. Further, the problems arising from class imbalance in the schedule caused due to less frequently performed activities are also explored and addressed. The models are calibrated and validated using the activity-travel diary data from the OViN 2016 dataset. To evaluate the robustness of the model, it is also tested on a time budget dataset with 23 different activity types. The results indicate that the proposed method can predict the distributions of activity start times and duration with reasonable accuracy. The results demonstrate that the proposed method can efficiently model activity schedules and can be a useful tool for travel demand modeling.

Differential impacts of autonomous and connected-autonomous vehicles on household residential location

High market penetration of autonomous vehicles (AVs) and connected-autonomous vehicles (CAVs) is expected to impact transportation network performance, which is an important determinant of residential location decisions, especially for households who commute to work by personal vehicle. This study examines and compares the impacts of privately owned AVs and CAVs on the location and commute characteristics as well as the spatial distribution of households within the Triangle Region in North Carolina. A Mixed Multinomial Logit model is developed using recent household survey data to capture household preferences. In addition, the region’s travel demand model, the Triangle Regional Model, is used to predict the network-level impacts of AV and CAV adoption, and cluster analysis is conducted to explore how network performance changes vary with transportation demand and supply zone characteristics at a local and regional level. Residential location patterns are predicted for a number of AV and CAV scenarios for the year 2045 using the outputs of the econometric analysis and the Triangle Regional Model. We find that extensive adoption of private CAVs improves network conditions and encourages households to live farther from work, leading up to a 5.6% increase in suburban and rural households that commute to work by personal vehicles. A high market share of AVs is associated with deteriorated transportation network performance and up to a 2.8% increase in urban households. Results vary by market penetration rate of each technology, mix of AVs, CAVs, and human driven vehicles in the traffic stream, and fuel type (conventional-fuel versus electric vehicles).

Introduction of emerging mobility services in rural areas through the use of mobile network data combined with activity‐based travel demand modelling

AbstractWhilst urban areas are thriving in trialling new mobility services (NMS), rural environments, often perceived as areas of low demand for travel, struggle to attract investments for creating more mobility solutions alongside traditional public transport (PT) services, making residents more reliant on private cars.This paper describes how policy interventions for introducing NMS in rural areas should be guided by big data to capture real and accurate travel behaviours, therefore avoiding perceived biases and potentially underestimating demand.In the UK, the provision of transport in rural areas is solely linked to population density and does not consider differences between places and residents’ travel habits. The proposed data‐driven decision‐making process used trip‐chains from mobile network data (MND) to derive recent and accurate travel patterns from residents and provide the right mix of on‐demand mobility services alongside existing fixed scheduled public transport (PT).The manuscript describes the steps carried out to study three rural areas at low, medium and high population density in the UK: a data landscape to select study areas; the development of an activity‐based model, which uses anonymised mobile network data (MND) aggregated at trip‐chains level to derive travel patterns; and the development of an on‐line questionnaire and focus groups with rural communities to co‐designing solutions based on attitudes towards NMS.Results demonstrated that a data‐driven decision making process to introduce NMS is a viable solution for updating demand for travel in rural areas, offering a broad understanding of mobility needs and the relationship of interdependency with nearby areas, therefore allowing policy makers to create users‐centric transport solutions.The study concludes by drawing recommendations for NMS for passengers and goods for the NMS proposed for a rural areas [Demand Responsive Transport (DRT), Micro‐mobility and delivery drones].

The perfect match? Assessment of excess commute and transport externalities using an agent-based transport model

Excess commute (EC) refers to the sub-optimal allocation of workers to jobs in an urban region in terms of minimal commute distances. This article investigated EC for the Munich metropolitan area using a microscopic agent-based modeling suite. We first considered the optimization strategy, comparing the traditional zone-based transportation problem to a microscopic routine. Zone-based optimization is computationally efficient, but it underestimates EC due to spatial biases, even when the zone system is highly disaggregate. We then applied the optimized job-worker assignment to a microscopic travel demand model, assigning workers to jobs of the same job sector at a shorter distance from their home, accounting for multi-worker households. With shorter commutes, there was a substantial shift toward active modes. However, we only observed moderate impacts on total travel demand, with the overall effect dampened due to compensatory behavior between mandatory and discretionary travel. Finally, the outputs from the demand model were used by a microscopic assignment model to calculate externalities attributable to EC. Because of the complexity of travel demand, we conclude that the EC indicator alone would overestimate the benefits of policies targeting jobs-housing balance.

Transit Benefit Index: A Comprehensive Index for Capturing Externalities in Transit Planning

This research provides a methodology for estimating the total societal benefit generated from substituting private vehicle trips with public transportation trips. The external costs of private and public transportation were estimated using a base case travel demand model and then a mode shift was simulated to calculate the effects of shifting one full transit unit (e.g., bus) of demand from a private to public mode. This shift was performed for all origin–destination (O-D) pairs in a region to find the O-D pairs that resulted in the greatest net benefit. These benefits were then normalized using the total automobile vehicle kilometers traveled removed from the network to generate a “transit benefit index.” This methodology was applied to a case study of the city of Bogotá, Colombia. A total of 102 scenarios were simulated: a 2 in base case, and 10 total sensitivity analyses, each including two transit provision alternatives. The results were contrasted with the cost of a new transit unit—a new bus in this case—revealing that the total economic benefit derived from 1 year of increased transit ridership was larger than the financial cost of a new bus to the transit operator. These results suggest that the City of Bogotá should consider further subsidies to transit fares to increase ridership and mitigate externalities.

A simulation-based optimization approach for designing transit networks

Public transport network design deals with finding efficient network solution(s) from a set of alternatives that best satisfies the often-conflicting objectives of stakeholders like passengers and operators. This work presents a simulation-based optimization (SBO) model for designing public transport networks. The work’s novelty is in developing such a network design model that fully accounts for the stochastic behavior of commuters on the transit network. The SBO discipline solves decision-based problems like the transit network design problem (TNDP) by combining simulation and optimization models. The proposed model integrates a disaggregated activity-based travel demand simulation with a multi-objective network optimization algorithm. Trip-based travel demand models are commonly used to represent traveler behavior in the literature. The approach limits its ability to accommodate the stochastic realities of traveler behavior in a transit network design solution. Using activity-based simulation instead makes it possible to account for a more realistic traveler behavior, especially real-time decisions made in response to changing network dynamics which ultimately affect the distribution of demand over time on the network. The proposed model is applied to the improved design of the integrated public transport network in the City of Cape Town, South Africa. The results show SBO can design efficient network solutions that reflect the objectives of network stakeholders.

Towards seamless door-to-door travelling within Europe: Transport operators’ and passengers’ perspectives

Seamless door-to-door air passenger multimodal transport system is a new concept in the transport industry, and that is why data on system interoperability is lacking. In this paper a qualitative research method is applied, in order to get the transport experts’ opinions and information on how the multimodal system should be designed. Although, they are aware that the collaboration should result in increase in the volume of passengers, the most challenging task is to convince transport operators to share the data among each other, due to the legal issues. To determine the customers’ perspective for making multimodal choices in their journeys, the travel demand model is developed, based on survey data. The model reveals the importance for policy makers and transport operators serving the airport to address the access time reliability by carefully considering the passengers’ preferences.

Transit Network Design Problem – Impact on Network Travel Time Using a Transportation Planning Model

This research aimed to address a problem of the Transit-NDP with an upper level that optimizes the vehicles' Network Travel Time (NTT) subject to a budget constraint, and the lower level estimates vehicles' NTT based on a travel demand model (TDM), which involves identifying the optimal transit projects for reducing the NTT. The study objective is to explore the impact of a naïve model of the more complex Transit-NDP, where stakeholders suggest a group of potential transit projects. A complete evaluation of the enumeration and sampling search strategy is conducted for the two test networks. A sensitivity analysis is conducted on the impact of a demand increase for each test network. The Halle Network exhibits an almost constant NTT reduction ranging between 47.64% and 50.79% while the Karlsruhe Network exhibits a different behaviour with an NTT reduction ranging between 39.70% and 52.40%. In both networks, a set of candidate transit projects were members of the best solution for all test runs. The sensitivity analysis demonstrated that some candidate transit projects selected at lower budget levels, demand levels, or both were not necessarily selected as the best solutions at upper budget, demand levels, or both. In addition, the resulting dataset of the enumeration and sampling was utilized to develop a Random Forest regression model that produces estimates of the NTT with a success rate of 90%.

Emerging trends in household travel survey programs

Transportation planners use household travel surveys to understand travel behavior, whether to develop forecasts of travel activity using travel demand or simulation models, to analyze personal motivations that drive the decision to travel, to identify users of existing transportation infrastructure and services, or to determine responsiveness to available or future travel options. Large urban area transportation planning organizations are faced with making decisions on their next household travel surveying effort to meet growing data needs. The New York Metropolitan Transportation Planning Council (NYMTC) has focused attention on surveying options, including conducting a pilot study to evaluate the ability to collect household travel data using a smartphone app. This research reviews current travel surveying strategies aimed at improving timeliness and accuracy, while reducing participant burden (e.g., using smartphone technologies). It also includes a vision for a long-term data program, facilitated by the establishment of a regional Community of Practice (CoP) to support multi-agency data collection efforts.