Regional Travel Demand Model Research Articles

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.

Modeling the Impacts of Low Emission Zone on Route Diversion and Emissions of Heavy-Duty Trucks: A Southern California Case Study

Heavy-duty diesel trucks (HDDTs), the majority of which are used for freight movement, are major contributors of nitrogen oxides (NO x) and fine particulate matter (PM2.5) emissions. As a result, communities close to freight hubs such as ports, railyards, and distribution centers are disproportionately affected by diesel-related air pollution. This study evaluates the potential for emission-based access restriction and pricing strategy to reduce HDDT emissions in one such community in Southern California, U.S. Using a regional travel demand model with a dedicated heavy-duty truck submodel, it is assumed that a $10 emission fee is collected from HDDTs that do not meet the 2007 emission standards (i.e., model years 2009 and older) when they enter the community, which is considered to be a low emission zone (LEZ). The modeling results show that the emission fee diverts 60% of the pass-through trips made by HDDTs that do not meet the required emission standards away from the LEZ. The emission fee is found to reduce NO x, PM2.5, and carbon dioxide (CO2) emissions from HDDTs inside the LEZ by 37%, 38%, and 25%, respectively. Also, it is found that the emission fee has minimal impacts on the total emissions in the modeling area. The total NO x and CO2 emissions remain unchanged, while the total PM2.5 emission increases by 1%. These results demonstrate that emission-based access restriction and pricing can be an effective strategy for reducing truck emissions in communities heavily affected by truck traffic without resulting in adverse impacts on the regional emission inventory.

Towards transit equity in Detroit: An assessment of microtransit and its impact on employment accessibility

• An equity analysis of a hypothetical microtransit service in Metro Detroit. • The performance measure used is logsum-based employment accessibility. • We found that urban residents are likely to experience the highest accessibility gains. • Microtransit is likely to help reduce the equity gap in accessibility. Several studies cite great potential for microtransit services to improve transportation equity by providing disadvantaged communities with a more flexible and reliable option relative to traditional fixed route transit. However, few have attempted to forecast these equity benefits. This study seeks to contribute to the ongoing discussion by exploring potential equity impacts of a microtransit service in Metropolitan Detroit. Using a logsum accessibility measure calculated from a regional travel demand model, we simulate accessibility changes due to a hypothetical regional microtransit service and demonstrate a regional transportation equity analysis using a typical regional travel demand model. Our results show that accessibility gains would be slightly higher for lower income communities (17% increase compared with 13% for high income) and transit-dependent households (21% compared with 15% for car-owning households). Further, we find that Microtransit may help to reduce gaps in accessibility between disadvantaged and more advantaged traveler groups.

Temporary Migration Flow Inference and Analysis From Perspective of Mobile Phone Network Data

Temporary migration trips are circulatory journeys that can be associated with tourism as these trips aren’t made on a regular basis. They’re often left unmeasured due to limited data sources, and thereby missing from regional and national travel demand models. This article presents a methodology for inferring large-scale temporary migration trips from mobile phone network data or Call Detail Record (CDR), and analyzing their spatial determinants based on urban assets derived from Google Places data. As a case study, CDR of mobile phone users in Portugal was used in our study from which insightful statistical patterns of both intra and inter-district flows were observed. Analysis of spatial determinants shows that places for leisure and population density are among the top influential factors that attract inflows into the district, while places for public service such as city hall, local government office, courthouse, and cemetery are negative urban assets for attracting district’s inflows. The presented methodology and insights gained from our analysis can be useful for transport and urban planners to establish better informed travel demand modeling and urban planning strategies.

Impacts of highly automated vehicles on travel demand: macroscopic modeling methods and some results

Automated vehicles (AV) will change transport supply and influence travel demand. To evaluate those changes, existing travel demand models need to be extended. This paper presents ways of integrating characteristics of AV into traditional macroscopic travel demand models based on the four-step algorithm. It discusses two model extensions. The first extension allows incorporating impacts of AV on traffic flow performance by assigning specific passenger car unit factors that depend on roadway type and the capabilities of the vehicles. The second extension enables travel demand models to calculate demand changes caused by a different perception of travel time as the active driving time is reduced. The presented methods are applied to a use case of a regional macroscopic travel demand model. The basic assumption is that AV are considered highly but not fully automated and still require a driver for parts of the trip. Model results indicate that first-generation AV, probably being rather cautious, may decrease traffic performance. Further developed AV will improve performance on some parts of the network. Together with a reduction in active driving time, cars will become even more attractive, resulting in a modal shift towards car. Both circumstances lead to an increase in time spent and distance traveled.

Big data, accessibility and urban house prices

Big data applications are attracting increasing interest among urban researchers. One unexplored question is whether the inclusion of big data accessibility indices improves the accuracy of hedonic price models used for residential property valuation. This paper compares a big data index with an index derived from a regional travel demand model developed by local transportation planning agencies and traditional measures of accessibility defined as distances to employment centres. Controls for submarkets and a combined spatial autoregressive and spatial error model are also assessed as tools for capturing the value of location. Using single-family residential transactions from the Miami, Florida, metropolitan area, the study’s main conclusion is that the big data accessibility measure does not add meaningful explanatory or predictive power. In contrast, the spatial autoregressive and error model outperforms the other options considered.

Smart charging management for shared autonomous electric vehicle fleets: A Puget Sound case study

Increasingly, experts are forecasting the future of transportation to be shared, autonomous and electric. As shared autonomous electric vehicle (SAEV) fleets roll out to the market, the electricity consumed by the fleet will have significant impacts on energy demand and, in turn, drive variation in energy cost and reliability, especially if the charging is unmanaged. This research proposes a smart charging (SC) framework to identify benefits of active SAEV charging management that strategically shifts electricity demand away from high-priced peak hours or towards renewable generation periods. Time of use (TOU), real time pricing (RTP), and solar generation electricity scenarios are tested using an agent-based simulation to study (1) the impact of battery capacity and charging infrastructure type on SAEV fleet performance and operational costs under SC management; (2) the cost reduction potential of SC considering energy price fluctuation, uncertainty, and seasonal variation; (3) the charging infrastructure requirements; and (4) the system efficiency of powering SAEVs with solar generation. A case study from the Puget Sound region demonstrates the proposed SC algorithm using trip patterns from the regional travel demand model and local energy prices. Results suggest that in the absence of electricity price signals, SAEV charging demand is likely to peak the evening, when regional electricity use patterns already indicate high demand. Under SC management, EVs with larger battery sizes are more responsive to low-electricity cost charging opportunities, and have greater potential to reduce total energy related costs (electricity plus charging infrastructure) for a SAEV fleet, especially under RTP structure.

Incorporating features of autonomous vehicles in activity-based travel demand model for Columbus, OH

Autonomous vehicles (AVs) could change travel patterns of the population significantly and with the rapid improvements in AV technology, transportation planners should address AV impacts in regional plans and project evaluations for the mid-term and long-term horizons (10–15 years and beyond). There are multiple travel model components from demand generation to network assignments that need to be modified, updated, or added to fully capture the potential impacts of AVs on regional travel patterns. This paper describes how the features of AVs were incorporated in the regional Activity-Based travel demand Model developed for Columbus, OH, metropolitan region. The model modifications included multiple adjustments to the travel demand sub-models, network assignments, as well as an addition of a new sub-model for vehicle routing and parking that addresses such new phenomenon as empty AV relocation trips. Due to many factors of uncertainty associated with AVs, a scenario-based approach was adopted for evaluation of the potential impacts of AVs on the travel patterns. The emphasis of the scenario analysis was on multiple dimensions of travel behavior in addition to such aggregate regional measures as VMT, etc. The paper presents an analysis of potential impacts of AVs on accessibility measures, activity participation, tour formation, and mode choice. The scenario analysis applied to the Columbus region showed overall logical potential impacts of AVs with many insights useful for transportation planning.

Exploratory Modeling and Analysis for Transportation: An Approach and Support Tool - TMIP-EMAT

Traditionally, travel forecasting models have been used to provide single point predictions. That is, a single future scenario is developed and the model is applied to that scenario. This approach, however, ignores the deep uncertainty that exists in future land use, demographic, and transportation systems inputs, not to mention the uncertainty that exists in the model itself. More importantly, transportation policy decisions made on the basis of such model outputs may be misguided and ineffective. This paper demonstrates and motivates the use of travel forecasting models in an exploratory manner that accounts for the inherent uncertainties of the future. Specifically, this paper describes the user workflow for a new planning and modeling tool: the Travel Model Improvement Program Exploratory Modeling and Analysis Tool (TMIP-EMAT) that has been developed to facilitate the use of exploratory techniques with travel forecasting models. Examples from the proof of concept deployment using the Greater Buffalo-Niagara Regional Transportation Council regional travel demand model are included. The goal of the longer term study is to provide TMIP-EMAT for state and regional transportation planning agencies to assess how technological innovations will affect traffic and transit demand on major corridors 20 to 30 years down the road. The tool will illuminate interactions between transportation supply and demand on urban surface transportation systems (especially at the corridor level) through exploratory modeling and simulation, and facilitate insights into potential, possible, plausible, probable or preferred futures.

Examining the Equity Impacts of Autonomous Vehicles: A Travel Demand Model Approach

As investments in autonomous vehicle (AV) technology continue to grow, agencies are beginning to consider how AVs will affect travel behavior within their jurisdictions and how to respond to this new mobility technology. Different autonomous futures could reduce, perpetuate, or exacerbate existing transportation inequities. This paper presents a regional travel demand model used to quantify how transportation outcomes may differ for disadvantaged populations in the Washington, D.C. area under a variety of future scenarios. Transportation performance measures examined included job accessibility, trip duration, trip distance, mode share, and vehicle miles traveled. The model evaluated changes in these indicators for disadvantaged and non-disadvantaged communities under scenarios when AVs were primarily single-occupancy or high-occupancy, and according to whether transit agencies responded to AVs by maintaining the status quo, removing low-performing routes, or applying AV technology to transit vehicles. Across the performance measures, the high-occupancy AV and enhanced transit scenarios provided an equity benefit, either mitigating an existing gap in outcomes between demographic groups or reducing the extent to which that gap was expanded.

How accurate is the regional travel demand model in mimicking real-world travel times?

ABSTRACTThis paper provides insights pertaining to the validity of a regional travel demand model in mimicking real-world travel times. The estimated travel times from the regional travel demand model, for the base year 2015, for Mecklenburg County in the city of Charlotte, North Carolina (NC) were compared with travel time statistics from a private data source, for the same year. The results indicate that the estimated travel times from the regional travel demand model are typically lower than the 85th percentile travel times, irrespective of the link speed limit. The estimated travel times for the Central Business District (CBD) area type are moderately correlated with the travel time statistics from the private data source, irrespective of the time of the day. For all the other area types, stronger correlations were observed when the estimated travel times from the regional travel demand model are compared with 10th to 50th percentile travel times. The calculated Pearson correlation coefficients are low for morning and evening peak periods compared to mid-day and night-time period, indicating the inability of the regional travel demand model in mimicking congested traffic conditions accurately.

Using Indicators to Assess Sustainable Transportation and Related Concepts

The overall goal of this study is to assess the concept of sustainability in relation to the related concepts of “health” and “livability” that have emerged in transportation planning discourse. This study achieves the goal using an indicator-based case study, conducted for the El Paso metropolitan area in the United States. Data from the regional travel demand model and other sources were used to quantify a sustainability index, livability index, and health index for individual traffic analysis zones in the region, for four analysis years over a 30-year planning horizon. Each index was comprised of representative indicators, which were normalized and aggregated in accordance with common multi-criteria decision-making methods. The analysis results demonstrated little correlation between the quantified livability, sustainability, and health indices developed for the El Paso region. The indices also showed relatively low levels of change over time for a location. That is, the relative performance of a traffic analysis zone tended to stay the same, despite the modeled changes to the transportation system, demographics, and land use. The main implication of the research findings is that despite overlaps at a theoretical level, concepts such as livability and health cannot necessarily serve as proxies for sustainability when implemented in practice. The study also provides insight into the challenges of making meaningful change in the area of sustainability over time and highlights the influence of factors beyond transportation, such as land use and socio-economic issues.

Making Strides: State of the Practice of Pedestrian Forecasting in Regional Travel Models

Much has changed in the 30 years since non-motorized modes were first included in regional travel demand models. As interest in understanding behavioral influences on walking and policies requiring estimates of walking activity increase, it is important to consider how pedestrian travel is modeled at a regional level. This paper evaluates the state of the practice of modeling walk trips among the largest 48 metropolitan planning organizations (MPOs) and assesses changes made over the last 5 years. By reviewing model documentation and responses to a survey of MPO modelers, this paper summarizes current practices, describes six pedestrian modeling frameworks, and identifies trends. Three-quarters (75%) of large MPOs now model non-motorized travel, and over two-thirds (69%) of those MPOs distinguish walking from bicycling; these percentages are up from nearly two-thirds (63%) and one-half (47%), respectively, in 2012. This change corresponds with an increase in the deployment of activity-based models, which offer the opportunity to enhance pedestrian modeling techniques. The biggest barrier to more sophisticated models remains a lack of travel survey data on walking behavior, yet some MPOs are starting to overcome this challenge by oversampling potential active travelers. Decision-makers are becoming more interested in analyzing walking and using estimates of walking activity that are output from models for various planning applications. As the practice continues to mature, the near future will likely see smaller-scale measures of the pedestrian environment, more detailed zonal and network structures, and possibly even an operational model of pedestrian route choice.

Assessing public transit service equity using route-level accessibility measures and public data

The benefits and burdens of public transit service changes can be quantified using many different metrics. In the United States, the Federal Transit Administration requires transit agencies to assess the equity of proposed service changes using the demographic shares of affected riders. The purpose of the work presented here is to inform the development of more robust transit equity analyses than are currently conducted by integrating measures of accessibility – the ease with destinations can be reached – into FTA-required analyses. The measures are calculated using publicly available data, including the US Census Bureau's Longitudinal Employer-Household Dynamics dataset and transit route and schedule information in the General Transit Feed Specification (GTFS) format. The results demonstrate that relying on a single measure (e.g. population shares or accessibility) to associate a route with a particular demographic group is likely to be deficient. Previous academic work on accessibility has not translated well to practice in part because the calculation of accessibility relied upon regional travel demand model outputs that were difficult to obtain. This work thus fills an important gap in the literature and practice by tying advances in the academic literature to FTA-mandated analysis with publicly available data.

Integrating travel demand modeling and flood hazard risk analysis for evacuation and sheltering

In this paper, the risks of flooding hazards to the transportation system in urban Honolulu are assessed using a regional travel demand model (TDM). The approach serves to support understanding of evacuation and sheltering needs. Coastal and inland flooding hazard scenarios were modeled with four different hazard conditions. The inundation defined by the Maximum Envelope of High Water (MEOW) based on a hurricane that struck Hawaii and a tsunami run-up generated by historical earthquake events combined with a one-meter rise in sea level provided the worst possible coastal flooding scenario. A 500-year return period riverine flooding scenario was also included. The critical flooding hazard was transformed into a uniform 100 by 100meter GIS based grid system. The Oahu Metropolitan Planning Organization (OMPO) TransCAD model for 2035 provided the origin and destination trips for eleven trip types including motorized and non-motorized travel modes. The OMPO model includes personal travel, travel to the airport, commercial vehicle travel and visitor travel sub-models. Origin and destination trip matrices for Traffic Analysis Zones (TAZ) and highway and transit assignment details were extracted from the model. The O-D trips and road segment traffic details were transformed to the spatial grid system and overlaid on the flooding hazard layer. The spatial categorization of the flooding along with the trip and travel data from the TDM provided a robust method to quantify and visualize risks to travelers and the transportation system. The analysis shows that flooding hazard scenarios have serious risks in Honolulu. First, a large portion of the study area is susceptible to flooding, threatening the population, economy, and infrastructure. Second, in addition to areas susceptible to flooding, a larger percentage of origins, destinations, trips and vehicle miles of travel (VMT) are affected because of the need to travel through the at-risk areas. Third, commercial, transit and non-motorized trips are disproportionately affected compared to auto travel. This paper demonstrates how hazard data and risk models can be integrated with travel demand models for purposes of evacuation planning and sheltering as well as emergency planning and hazard mitigation and adaptation of the transportation system to climate change.

Developing high-resolution urban scale heavy-duty truck emission inventory using the data-driven truck activity model output

Air quality modelers often rely on regional travel demand models to estimate the vehicle activity data for emission models, however, most of the current travel demand models can only output reliable person travel activity rather than goods/service specific travel activity. This paper presents the successful application of data-driven, Spatial Regression and output optimization Truck model (SPARE-Truck) to develop truck-related activity inputs for the mobile emission model, and eventually to produce truck specific gridded emissions. To validate the proposed methodology, the Cincinnati metropolitan area in United States was selected as a case study site. From the results, it is found that the truck miles traveled predicted using traditional methods tend to underestimate — overall 32% less than proposed model— truck miles traveled. The coefficient of determination values for different truck types range between 0.82 and 0.97, except the motor homes which showed least model fit with 0.51. Consequently, the emission inventories calculated from the traditional methods were also underestimated i.e. −37% for NOx, −35% for SO2, -43% for VOC, −43% for BC, −47% for OC and - 49% for PM2.5. Further, the proposed method also predicted within ∼7% of the national emission inventory for all pollutants. The bottom-up gridding methodology used in this paper could allocate the emissions to grid cell where more truck activity is expected, and it is verified against regional land-use data. Most importantly, using proposed method it is easy to segregate gridded emission inventory by truck type, which is of particular interest for decision makers, since currently there is no reliable method to test different truck-category specific travel-demand management strategies for air pollution control.

Application of Traffic Thermostat for I-30 Managed Lanes in Dallas, Texas

Managed lanes have historically experienced an evolution in operational policies. Over time, speeds on a managed lane may degrade because of oversubscribed demand. Operators have strategies to mitigate problems and maintain optimal performance; these strategies include changing the occupancy requirement, varying tolls (if the facility is priced), and altering transit service. Assessing the potential impact of operational strategies is a challenge. The traffic thermostat tool is a software-based guide that helps select specific strategies projected to influence the metrics for each goal. This paper reports on how the tool was adapted for use on the I-30 managed lanes in Dallas, Texas, a high-occupancy-toll lane with reduced toll rates for qualifying carpools. An example scenario shows the consideration of travel speed and throughput goals, with acceptable performance thresholds of 50 mph and 5,700 persons per hour for the entire facility. The thermostat estimated values of projected speed and throughput for selected operational fixes by using the calibrated speed–flow relationship from the regional travel demand model. Results from a quantitative travel survey helped to derive mode shift elasticities for use by the tool. Overall, the dynamic nature of demand, diversity of user groups, ambiguity with exogenous factors (e.g., regional unemployment and fuel prices), and need for extensive data on the lanes led to uncertainty and difficulty with prediction capability. However, use of the traffic thermostat can show policy makers and others the inherent challenge of performance management for managed-lane facilities.

Using Google’s Aggregated and Anonymized Trip Data to Support Freeway Corridor Management Planning in San Francisco, California

With rising urban freeway congestion and limited funds available for highway expansion, it may be essential to manage traffic growth by using high-occupancy toll lanes and other travel demand management (TDM) measures. To prepare for and help guide freeway corridor management planning in the US-101 and I-280 corridors in San Francisco, California, information describing trip origins and destinations by time of day was desired. Observed roadway facility-specific origin–destination (O-D) flows can help researchers to understand spatial distribution of demand and impute willingness to pay, actions that are useful in evaluating various TDM strategies. This paper describes a new passively collected O-D data source—Google’s aggregated and anonymized trip (AAT) data—obtained under Google’s Better Cities program. Aggregate hourly flow matrices for 85 districts covering California’s nine-county Bay Area specific to four freeway segments in San Francisco were obtained. Because AAT data account for only a sample of travelers, Google provides relative flows rather than absolute counts. Linear regression models were estimated to relate relative flows in the AAT data set and observed traffic volumes from the California Department of Transportation’s Performance Measurement System. The models were applied to convert relative flows to trips and derive facility-specific, time-dependent O-D matrices. Comparison of these facility-specific O-D matrices to select link O-D matrices from a regional travel demand model show that there is a higher correlation in terms of productions at origin districts and attractions at destination districts than at the O-D flow level. Some opportunities and limitations of the new data source are discussed, along with recommendations for future research.

Development of destination choice models for pedestrian travel

Most research on walking behavior has focused on mode choice or walk trip frequency. In contrast, this study is one of the first to analyze and model the destination choice behaviors of pedestrians within an entire region. Using about 4500 walk trips from a 2011 household travel survey in the Portland, Oregon, region, we estimated multinomial logit pedestrian destination choice models for six trip purposes. Independent variables included terms for impedance (walk trip distance), size (employment by type, households), supportive pedestrian environments (parks, a pedestrian index of the environment variable called PIE), barriers to walking (terrain, industrial-type employment), and traveler characteristics. Unique to this study was the use of small-scale destination zone alternatives. Distance was a significant deterrent to pedestrian destination choice, and people in carless or childless households were less sensitive to distance for some purposes. Employment (especially retail) was a strong attractor: doubling the number of jobs nearly doubled the odds of choosing a destination for home-based shopping walk trips. More attractive pedestrian environments were also positively associated with pedestrian destination choice after controlling for other factors. These results shed light on determinants of pedestrian destination choice behaviors, and sensitivities in the models highlight potential policy-levers to increase walking activity. In addition, the destination choice models can be applied in practice within existing regional travel demand models or as pedestrian planning tools to evaluate land use and transportation policy and investment scenarios.

Assumptions Inherent in Assessing Traffic Forecast Accuracy

A dozen approaches such as extrapolation of previous traffic counts, trip rates based on expected future land development, and application of regional travel demand models can be used to forecast traffic volumes. Because of Virginia’s interest in knowing the accuracy of forecasting approaches, the researchers compared previous Virginia forecasts with observed volumes. These comparisons revealed five observations affecting how accuracy is evaluated. First, the statistic used to aggregate individual link forecast errors may influence the perception of accuracy; for one study, two percentage-based statistics—the median percentage error and the mean absolute percentage error—yielded errors of 2% and 43%, respectively. (An implication is that the appropriate error statistic depends on the forecast’s purpose.) Second, the accuracy of different types of traffic forecasts varies by magnitude; the median absolute percentage error for Virginia studies ranged from 12% (for a site-specific land development study) to 72% (for statewide forecasts based on historic traffic volumes). Third, because of differences in geometry and the frequency of counts, there may not be a direct relationship between a link’s forecast and its observed volume; in one study, the error ranged from almost 0% to 36%, depending on how the forecast and observed volumes were compared. Fourth, some methods require input data that must be forecast, and different indications of accuracy result (e.g., mean absolute percentage errors of 13% versus 34% for the Fratar method) depending on whether the input data are forecast with or without error. Fifth, depending on the chosen decision criterion, large errors may not necessarily affect the actions taken.