Travel Demand Model Research Articles

GPS-supported smartphone app-based integrated travel diary and time-use data collection: challenges and lessons learned

AbstractTravel behaviour and time-use data are two vital data sources for travel demand modelling. Travel behaviour is traditionally collected through household travel surveys, enhanced by using GPS-supported smartphone apps for passive location data collection. However, recruiting individuals willing to install these apps with sustained motivation to continue participation has been a critical challenge. This paper shares insights from a travel and time-use data collection procedure in Chicago and Sydney using the Fourstep app. Social media platforms were utilised as a solution to recruit participants in Chicago, where an international market research company failed to accomplish the task. This paper also discusses the challenges we faced and suggests ways to overcome them, offering valuable guidance to researchers in recruiting participants for smartphone application-based data collection. It also offers an analysis of travel, time-use, and travel-based multitasking behaviours based on the data collected from the Chicago and Sydney samples.

Week-long activity-based modelling: a review of the existing models and datasets and a comprehensive conceptual framework

ABSTRACT Activity-based travel demand models emerged mainly to fix the conceptual, statistical, and operational deficiencies of conventional trip-based models. This is done by microstimulating the activity scheduling behaviour of individuals/households instead of modelling the number of trips between the zones of an urban area. In the “Next Generation” of activity-based models (ABMs), researchers are making an effort to improve their capacity to replicate the travel-activity patterns of urban populations more realistically. Expanding the modelling time frame from a single day to an entire week is one of the essential aspects of the “Next Generation” of ABMs. Although there is still a long way to go before a comprehensive and operational week-long ABM can be developed, the literature on its different aspects, the theoretical and conceptual frameworks, and the efforts to collect multi-day travel-activity diaries are now at a stage that is worth a comprehensive and systematic review. Therefore, the current study is devoted to exploring the existing literature on multi-day activity-based modelling, categorising its elements in a systematic manner, searching for the research gaps in the existing models and proposing a comprehensive framework to fill those gaps.

Using App Usage Data From Mobile Devices to Improve Activity-Based Travel Demand Models

Using App Usage Data From Mobile Devices to Improve Activity-Based Travel Demand Models

Analyzing the importance of network topology in AADT estimation: insights from travel demand models using graph neural networks

Analyzing the importance of network topology in AADT estimation: insights from travel demand models using graph neural networks

Understanding Party Size in Ride-Hailing: Solo Versus Group Travel

Emerging mobility technologies, like ride-hailing, are underrepresented in travel behavior surveys. Consequently, travel demand modelers have often resorted to simplistic solo-traveler assumptions or relied on aggregate trip size factors within activity- and agent-based models. To address this gap and help modelers understand ride-hail party size, this study analyzed data collected from a 2021 to 2022 household travel survey in the greater Twin Cities (Minneapolis–St. Paul, MN) region. Ride-hail trips were paired with person-, household-, and trip-level attributes to provide context for both single-party and multiparty ride-hailing. Binary logistic regression models indicated that shared micromobility users, households with more children, renters in large multifamily buildings, and individuals with at most a high school education were significantly more likely to use ride-hailing for group travel than for solo trips. A hurdle regression for party size count revealed that lower-income households, while less likely to use ride-hailing, were more likely to opt for group travel and form larger parties than other income cohorts. These findings offer insights into who takes ride-hailing for group travel, enabling policy makers to devise strategies targeting solo, unshared ride-hailing without negatively affecting large groups.

Cycling or Ropeway – Two Choices of Environmentally Friendly Urban Modes

Ropeways have proven to be a suitable extension to public transport in Latin American cities. Numerous ropeway projects are being discussed in Europe. These have not yet progressed beyond the planning stage because, among other reasons, no assessments of potential demand have been carried out. Current travel demand models do not consider mode-specific properties of ropeways and cycling competing with public transport. Based on a stated choice experiment, a mode choice model for people without car availability was estimated. Besides standard attributes like travel times and costs, the impact of mode-specific attributes like service rates, crowding, reliability, personal attitudes and weather conditions were tested. A Mixed-Logit model was estimated showing significant influence of these attributes on mode choice in Graz, an Austrian city with a mode share of 20% each for cyclists and public transport. The introduction of a ropeway can increase the share of public transport, as the high capacity and reliability of ropeways have a positive effect. However, it is unlikely that some groups of cyclists will use the ropeway, as travel time and cost are factors that are more important. The database and the modeling approaches can also be applied to other ropeway projects.

Evaluating the influence of cyclists’ route choices incorporation into travel demand modelling: A case study in greater Helsinki

Cycling is a sustainable transport mode that endorses an active lifestyle. While cycling shows great potential, it is essential for urban planning to consider attributes influencing the choices that cyclists act upon. Cyclists’ route choices have been studied since the Eighties with knowledge being applied in cycling network planning. Yet, the role of cycling as a sustainable transportation mode has been largely absent from travel demand modelling. This paper researches cyclists’ route choice preferences and evaluates the opportunity of incorporating route choice modelling into travel demand modelling to improve the accuracy of cycling route choice. To this end, a route choice framework is developed in which a stated preference survey for data collection is conducted, a multinomial Logit model is applied to the data to identify the factors that significantly influence cyclists’ route choice behaviour. The generated route choice utility models are further integrated into an existing regional travel demand model to evaluate the performance of cyclists’ route choice modelling in the presence of additional factors. Then, the route choice model outputs are validated against two sets of external data. The results show that bike facilities, traffic volume, and trip length are the key factors influencing cyclists’ route choice preferences, and the generated route choice models can be an applicable improvement in incorporating the influences of cyclists’ preferences into travel demand modelling.

Designing microtransit services in suburban areas: A case study in Palermo, Italy

Poor quality of Public Transport (PT) services is one of the main causes of social exclusion for people living in the suburbs. Public transport companies usually allocate few financial resources to these areas, providing transport services with low frequency, poor accessibility, poor reliability, and high waiting times at stops. Recently, microtransit has emerged as an effective solution to improve the travel experience in suburban areas, particularly for non-commuting trips during off-peak hours. This paper presents an integrated methodological approach for designing microtransit services to meet the mobility needs of people living in low-density suburbs. By conducting a Reveled Preference (RP) and Stated Preference (SP) survey and developing a travel demand model, the demand was estimated and used as input to simulate and size the service. Combining GIS and simulation models, Key Performance Indicators (KPIs) were assessed; fleet size to meet the trip requests was identified and the fare was selected using a sensitivity analysis. The method was applied to a real case study to design a new microtransit service with flexible routes and on-demand stops in a suburban area in Palermo, Italy. The results highlight how introducing a microtransit service with 30 nine-seater vans could change the mobility habits of people living in the suburban area, being attractive and financially sustainable if costing 2 €, or just a little more than the existing fixed-route bus service. It could improve the travel experience by reducing the average waiting time at stops to around 5 min and improve access to amenities and PT hubs by guaranteeing a walking time of maximum about 8 min.

Predicting transit ridership using an agent-based modeling approach

Accurate ridership estimation is pivotal in the advancement of sustainable transit systems, be it for proposed or existing transit networks. A multitude of methods, including travel demand models, direct ridership models, and regression models, have been employed by practitioners and researchers to estimate ridership at both station and network levels. However, travel demand models, frequently utilized for new transit lines, exhibit intrinsic limitations due to their aggregate nature and complexity based on their types. Researchers have also identified deficiencies, such as the incapacity to capture small spatial resolutions and specific station characteristics, as these models are predominantly designed for large-scale analyses.This study aims to overcome these limitations by introducing a novel approach that utilizes three microscopic agent-based models to develop a travel demand modeling suite, providing a policy-sensitive forecasting tool. The suite comprises three agent-based models: SILO-MITO-MATSim. Validation of the model against previous year data is conducted, and projections are made for future years. The model is applied to estimate network-level ridership for the proposed ‘Purple Line,’ a light rail transit line planned by MDOT, MTA, Maryland, which will integrate with the Washington D.C. Metro, the fourth largest transit system in the USA, boasting an average daily ridership of half a million. The study’s findings indicate an anticipated ridership of approximately 31,230 passengers in the inaugural year of 2027. The proposed model offers a robust and policy-sensitive solution empowering decision-makers to make informed choices to support a sustainable transportation system.

A travel demand modeling framework based on OpenStreetMap

Demand modeling is an important part of the setup of a traffic model for a city. All travel demand models rely on land use data as the demand for traveling fundamentally stems from activities occurring at different locations; however, many cities lack these data, or experience in estimating travel demand in their region. In response, this study develops a methodology for generating highly detailed land use data in the form of points of interest (POIs) specifically aimed at travel demand estimation purposes. The framework includes a procedure to extract, clean, enhance, and categorize freely available land use data from OpenStreetMap (OSM) into different POI categories, such as residences, schools, and shops. These residential and activity POIs, which are typical origins and/or destinations of trips, serve as the starting point for estimating travel demand. This paper demonstrates the framework’s utility through three case studies across different cities in Belgium. It validates the effectiveness of OSM-derived POIs for travel demand estimation by replicating Antwerp’s existing demand model, examines the POIs classification’s suitability for various travel demand purposes in Leuven, and assesses the transferability of correlations between OSM data and travel demand from Antwerp to Ghent. Beyond the applications illustrated in this paper, the framework provides opportunities for future research on the consistent disaggregation of existing zonal demand estimates and design-based research in which future demand is estimated given the development of POIs. The framework is openly available as a Python tool called Poidpy.

Developing an Integrated Activity-Based Travel Demand Model for Analyzing the Impact of Electric Vehicles on Traffic Networks and Vehicular Emissions

Developing an Integrated Activity-Based Travel Demand Model for Analyzing the Impact of Electric Vehicles on Traffic Networks and Vehicular Emissions

Modeling pedestrian activity in cities with urban network analysis

The global climate-change crisis, along with public health and economic competitiveness challenges in cities around the world have underscored the need for analytic tools to examine the relationship between city design and sustainable mobility. Car-centered travel demand models and land-use-transportation interaction models have historically analyzed zone-to-zone trips along major roadways, largely omitting pedestrian and bicycle trips and creating a gap in the ability for planners and urban designers to systematically assess non-motorized outcomes of development interventions. We present the Urban Network Analysis tools to address this gap. UNA tools offer an accessibility-based framework for analyzing how built environments influence pedestrian travel in both existing and newly planned built environments. Developed as a free plugin for Rhinoceros 3D since 2015 and applied in several cities and research projects internationally, this paper describes the current Urban Network Analysis modeling framework and discusses the unique contributions the framework offers compared to existing pedestrian modeling approaches. Using Somerville, MA as case example, we demonstrate several commonly used functions for planners: examining pedestrian accessibility over networks; identifying critical walking routes to destinations; estimating foot-traffic on street segments; identifying frustration points for pedestrians; and evaluating how development changes may impact pedestrian activity in their vicinity. Such analyses can provide analytic evidence to pedestrian infrastructure planning and investment, and enable planners, designers, and policy makers to prioritize projects that increase sustainable mobility outcomes.

Impact of autonomous vehicles on discretionary activities: an agent-based model with space–time accessibility constraints

AbstractThe extensive development of autonomous vehicles (AVs) is set to revolutionise the way of travelling. Research suggests that the introduction of AVs may affect travel behaviour and choices, resulting in long-term changes in land use. Accessibility is an important concept that connects transportation and land use, providing a holistic performance measure for the transport-land use system. However, this concept has not been adequately capitalised in studies that attempt to understand the impact of AVs on location choice decisions. To explore this knowledge gap, we proposed an agent-based simulation framework that integrates with accessibility constraints to study how AVs influence behavioural and location choices. The framework consists of an activity-based travel demand model with accessibility constraints and a dynamic transport assignment model. The accessibility constraints are derived from individuals’ travel time budgets based on activity-travel survey data. We applied the agent-based simulation framework to Clayton, Australia, and focused on discretionary activity location choices. Various values of travel time and vehicle running costs underpinned by the use of AVs were examined. While most studies have concluded that AVs can significantly increase trip lengths for daily activities, our results demonstrate that even when AVs are used, the movement of individuals is still limited by spatio-temporal constraints of accessibility. As a result, we predict that the increase in discretionary trip lengths and their impact on traffic congestion is modest.

Impact of electric vehicle charging demand on power distribution grid congestion

California, a pioneer in EV adoption, has enacted ambitious electric vehicle (EV) policies that will generate a large burden on the state's electric distribution system. We investigate the statewide impact of uncontrolled EV charging on the electric distribution networks at a large scale and high granularity, by employing an EV charging profile projection that combines travel demand model, EV adoption model, and real-world EV charging data. We find a substantial need for infrastructure upgrades in 50% of feeders by 2035, and 67% of feeders by 2045. The distribution system across California must upgrade its capacity by 25 GW by 2045, corresponding to a cost between $6 and $20 billion. While the additional infrastructure cost drives the electricity price up, it is offset by the downward pressure from the growth of total electricity consumption and leads to a reduction in electricity rate between $0.01 and $0.06/kWh by 2045. We also find that overloading conditions are highly diverse spatially, with feeders in residential areas requiring twice as much upgrade compared to commercial areas. Our study provides a framework for evaluating EVs' impact on the distribution grid and indicates the potential to reduce infrastructure upgrade costs by shifting home-charging demand. The imminent challenges confronting California serve as a microcosm of the forthcoming obstacles anticipated worldwide due to the prevailing global trend of EV adoption.

Understanding and including 'pink-collar' workers in employment-based travel demand models.

The segmentation of commuters into either blue or white-collar workers remains is still common in urban transport models. Internationally, models have started to use more elaborate segmentations, more reflective of changes in labour markets, such as increased female participation. Finding appropriate labour market segmentations for commute trip modelling remains a challenge. This paper harnesses a data-driven approach using unsupervised clustering-applied to 2017-20 South East Queensland Travel Survey (SEQTS) data. Commuter types are grouped by occupational, industry, and socio-demographic variables (i.e., gender, age, household size, household vehicle ownership and worker skill score). The results show that at a large number of clusters (i.e., k = 8) a highly distinct set of commuter types can be observed. But model run times tend to require a much smaller number of market segments. When only three clusters are formed (k = 3) a market segmentation emerges with one female-dominated type ('pink collar'), one male-dominated type ('blue collar') and one with both genders almost equally involved ('white collar'). There are nuances as to which workers are included in each segment, and differences in travel behaviours across the three types. 'Pink collar' workers are mostly comprised of female clerical and administrative workers, community and personal service workers and sales workers. They have the shortest median commutes for both private motorised and active transport modes. The approach and methods should assist transport planners to derive more accurate and robust market segmentations for use in large urban transport models, and, better predict the value of alternative transport projects and policies for all types of commuters.

How to build a more sustainable passenger air transport system: multimodal experience

A fully integrated passenger multimodal system is a new concept in the transport industry, and consequently, data on system interoperability is lacking. In this paper, a qualitative research method is applied, 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 an increase in the volume of passengers, the most challenging task is to convince transport operators to share the data with each other, due to legal issues. To determine the passengers’ perspective on making multimodal choices in their journeys, the travel demand model is developed, based on online survey data. The proposed (Binary Logistic Regression) model reveals the importance for policymakers and transport operators serving the airport to address access time reliability by carefully considering the passengers’ preferences in terms of their needs and age.

How does upzoning impact land use and transport: a case study of Seattle

ABSTRACT This work simulates the long-term land use and transportation impacts of upzoning in the region of Seattle, Washington at the parcel level, using UrbanSim and an agent-based travel demand model (SoundCast). Scenario results are compared to illuminate differences in a Business-as-Usual (BAU) future versus two upzoning scenarios that upzone all single-family-use residential parcels in the region or just those in the City of Seattle to 3 dwelling units per parcel. Under BAU, affordability falls 20% and rents rise 6% between 2014 and 2050, as the regional population grows to 5.7 million. With regional upzoning, 68,400 more dwellings are added, with rents 10% lower compared to the BAU future. The housing affordability index falls from 75.3 to 67.31, a significant improvement over the BAU scenario's sharper fall to 60.6. If only the City of Seattle upzones, only 3,400 additional units are predicted, but rents are 5.2% lower than the BAU case.

Comparing Implementation Strategies of Station-Based Bike Sharing in Agent-Based Travel Demand Models

Shared mobility solutions such as bike sharing services play a key role to reduce greenhouse gas emissions in urban areas. In this paper, we present an approach to model station-based bike sharing in the multi-modal agent-based travel demand model mobiTopp. We compare different implementations of how agents choose their bike pick-up and drop-off stations. In addition to two variations of distance minimization, we also present a gravity approach to represent the reliability of a system. By also comparing different behavioral attitudes of the agents towards walking, a total of six scenarios were implemented and tested. The presented approach allows to easily test scenarios with a varying number of bikes and stations. We apply our algorithm to a model for the city of Hamburg, Germany, where the mobility behavior of a total of 1.9 million agents is modeled. Our simulations show plausible results. The average distances, utilization shares of each station, and other parameters match with values from the actual service. While the different strategies result in significantly different access times, and provide further new valuable insights and options for parameterization, differences in resulting demand are small. Overall, this model provides new methods to simulate bike sharing in travel demand models, thus helps to simulate an important mode of transport of the future.

An OpenStreetMap-based approach for generating capacity-restricted POIs for activity-based travel demand modeling

The generation of Points of Interest including capacities for travel demand models is usually costly and laborious. Recently, several methods have been developed to automate this process based on Volunteered Geographic Information data as well as to validate the corresponding results. The methodology presented in this paper takes on the task of setting up such a workflow on a large scale, including capacities and all common activity types, by making use of OpenStreetMap data. As a result, a Points of Interest dataset could be created for the whole of Germany, which maps around 24 million destinations including capacities divided into seven activity types. An exemplary validation based on the generated workplace and shopping locations as well as capacities indicates a varying quality of results depending on the respective regional type. The discussion additionally concludes that assignment of several activities to one location presents challenges that should be addressed in future approaches.

Workshop synthesis: Data Fusion - Generating More Than a Sum of Parts

This report summarizes the insights from a workshop conducted during the 12th International Conference on Transport Survey Methods focusing on data fusion. The workshop discussion and the presentations illustrated that data fusion comes in many ways utilizing various data and methodologies. While much of the academic literature and practical applications of data fusion in transport apply to travel demand modelling, fusion in the context of travel survey data production is still in its infancy. Despite data fusion's acknowledged potential, a reason for this hesitancy is a lack of quality standards. The report recommends future research to improve knowledge about the suitability of data and methodologies for fusion and the development of respective standards. This will be necessary to convince stakeholders that the quality of fused data is not inferior to any single data source, be it travel survey data or, e.g. mobile phone data.