Real-world applications of planning, modeling, forecasting, and data analysis.
Incorporating User Heterogeneity in Value of Time (VOT) and Value of Reliability (VOR) for Managed Lanes
Md Sakoat Hossan, CDM Smith
Modeling Impacts of Autonomous Vehicles on Travel Demand with an Activity-Based Model
Peter Vovsha, WSP
Modeling Autonomous Transit Service in Statewide Transportation Networks
Sevgi Erdogan, University of Maryland, College Park
Macrolevel Analysis of Pedestrian and Bike Commuting: Using Self-Organizing Maps to Associate Crime, Poverty, and Demographics
Apoorba Bibeka, Texas A&M Transportation InstituteShow Abstract
Subasish Das, Texas A&M Transportation Institute
Michael Martin, Texas A&M Transportation Institute
Mohammad Jalayer, Rutgers, The State University of New Jersey
Sirajum Munira, Texas A&M Transportation Institute
Interest and emphasis on non-motorized modes of transportation, walking and biking, have been increasing in the recent years. However, studies focused on these modes have never received special attention by the current state of the art research environment. As a result, data availability on walking and biking is limited to perform any micro or macro level analysis. Much research is needed to find a way to effectively use available data sources to get data-driven insights on non-motorized commuting trips. The American Community Survey (ACS) provides one of the most comprehensive database on pedestrian and bicycle commuting for city planners and transportation professionals. In recent years, few studies have investigated the association between crime and safe biking and walking environment. Investigation on metropolitan cities with higher crime rates such as Chicago and Houston could shed more light on this association. This study developed a framework of using crime data, and ACS provided non-motorized commuting and related demographics to determine significant association patterns using Chicago data. An unsupervised data mining technique called Self Organizing Map (SOM) was used as it does not require any prior assumptions. The findings show that areas with lower income households are associated with high pedestrian and bicycle commuting. Negative association between crime and non-motorized commuting was also identified. The results also show that areas with larger younger populations are more likely to have more top non-motorized commuting trips. This study would provide new insights into the ongoing state of the art study designs and analysis methods associated with non-motorized travel mode.
Market Development of Autonomous Driving in Germany
Bernd Kaltenhaeuser, Baden-Wuerttemberg Cooperative State UniversityShow Abstract
Karl Werdich, Steinbeis Transfer Center Applied Methods of Project Management
Florian Dandl, Bundeswehr University, Munich
Klaus Bogenberger, Bundeswehr University, Munich
The present study examines the market penetration of autonomous cars for passenger transportation. For this, six models of mobility, which distinguish between technology levels as well as private and shared (taxi) usage, are defined. The focus is set on autonomous taxis without a steering wheel, whose market success depends on resistances against autonomous driving. These result from technological, juridical and economic circumstances as well as the users’ wishes. These were investigated using a survey of 353 participants. A system dynamics model was created and evaluated with data from the literature and the survey.
The results indicate that autonomous driving will be dominated by private vehicles within the scope of this study, which covers the time frame until 2040. These will mainly still be equipped with a steering wheel and are expected to reach 11.5 million units in Germany. Also, self-driving taxis are expected to enter the market, reaching their maximum of 1.5 million units already in 2031. As an autonomous taxi supports more people on average, the total number of vehicles is expected to drop from 45.1 million to around 37.7 million until 2040.
Pre-Destination Choice Walk-Mode Choice Models
Norman Marshall, Smart Mobility, Inc.Show Abstract
Walk mode share is large and likely is growing. Accurate walk trip forecasting is increasingly important for evaluating planning efforts intended to increase walk share. Currently, most travel demand models (trip-based or activity-based) model walk trips using post-destination-choice mode choice models. These models generally poorly match the locations of walk trips, because they do not model actual behavior. People do not look at all destinations, pick one, and then realize they cannot walk to it. The walk mode decision is made either simultaneously with the destination choice decision or prior to it.
Pre-destination-choice walk mode choice models have been found to be more accurate in models in several different regions. For example, the conventional Austin regional model only matches walk mode shares for work trips at the Census Tract level with an R-squared of 0.14 In sharp contrast, a simple pre-destination-choice walk mode choice model achieves an R-squared of 0.79. An even better model fit (R-square = 0.92) is achieved with only two independent variables in the Portland Maine region.
Modelers sometimes say that walk trips do not really matter because walk trips only substitute for short vehicle trips, or that walk trips do not matter because walk trips are mostly intrazonal trips. These observations are true of the models, but not true of the real world. In the real world, walk trips often substitute for longer auto trips that otherwise would have been made. Switching to pre-destination-choice walk mode choice modeling is not difficult, and is strongly recommended.
Low-Effort Techniques for Incorporating Driverless Vehicles in Legacy Regional Planning Models
Di Kang, Virginia Department of TransportationShow Abstract
John Miller, Virginia Department of Transportation
Regional travel demand models are an institutionalized element of the transportation planning process, requiring a multiyear investment from collaborating agencies that rely on model outputs to assist with project prioritization and community visioning. This paper reports on lessons learned in making modest modifications to one region’s legacy planning model to consider how driverless vehicles (DVs) may affect concerns expressed by regional stakeholders.
An outreach exercise suggested four congestion and emissions concerns: DVs might (1) initially reduce capacity (if operators choose comfortable acceleration rates); (2) later increase capacity (as platoons result); (3) increase travel by persons without access to a vehicle; and (4) increase zero occupant vehicles (as commuters avoid parking fees). The regional model incorporated these impacts through altering capacity, nonwork trips by persons age 65+, and commute travel. The results suggested that the concerns have different impacts on transportation performance. A capacity decrease yielded the greatest risk of congestion, increasing vehicle hours traveled (VHT) by 46% to 134%. By contrast, additional trips (to avoid parking charges) increased VHT by 22% to 30% and travel by persons age 65+ presently without access to a vehicle increased VHT by 1% to 2%. Because nitrogen oxide (NOx) emissions are parabolic with respect to speed, avoidance of parking charges increased NOx emissions by 10.8% to 12.2% whereas a capacity reduction increased NOx emissions by 5% at the most.
These results suggest that smaller metropolitan planning organizations can initially consider DVs by focusing on a few topics of local interest: although DV impacts and how best to represent them in the regional model remain uncertain, such models provide one way to begin to prioritize local concerns about DVs on an order of magnitude basis.
Building Desire Lines Using Delaunay Graphs
Pedro Camargo, Veitch Lister ConsultingShow Abstract
Mapping transportation flows in a region as straight lines between all Origin-Destination (OD) pairs, also known as Desire Lines, is one of the most readily available techniques for a modeler to interpret OD matrices when getting acquainted with new data and/or a new region. However, despite how easy it is to map Desire Lines, resulting maps are not visually appealing when the number of non-zero cells in the OD matrix is large, as is typically the case for realistic transportation models.
In order to circumvent the poor quality provided by standard Desire Lines, this paper proposes a novel algorithm for the creation of an alternative type of map with lines that do not overlap each other, leveraging procedures readily available on traditional GIS and transportation modelling packages.
This paper presents the details of the proposed algorithm, followed by the application of this tool to several test cases, from urban transportation models to regional freight models and at different aggregation levels for both the US and Australia.
Lastly, this paper discusses the implications derived from the utilization of the proposed algorithm and suggests additional research in this area. The full algorithm and implementation are provided as open source software in the form of an online repository and as a production-ready tool.
Street Intersection Characteristics and Their Impacts on Perceived Bicycling Safety
Kailai Wang, Ohio State UniversityShow Abstract
Gulsah Akar, Ohio State University
Safety concern is one of the core issues that deter people from bicycling in the US. Earlier studies have explored the associations between intersection design characteristics and bicyclist safety perceptions. Research shows that there are significant links between bicycling choice, safety perceptions, bicycling experience levels and socio-demographics. Yet, the existing bicycling safety-rating models do not control for the individuals’ socio-demographics and bicycling experiences that are known to affect bicycling choice. This study develops a Perceived Bicycling Intersection Safety (PBIS) model which helps engineers, planners and decision-makers better understand the contributions of a wide range of intersection features to bicyclists safety perceptions, controlling for socio-demographics and bicycling experiences. The empirical analysis is based on an online visual survey conducted at the main campus of The Ohio State University through March and April 2017. We determine that visual surveys are effective in capturing information about bicycling preferences. We conclude with recommendations for infrastructure decisions and suggestions for future research. The results of this study can help planners design street intersections that bicyclists prefer. Our model can be applied elsewhere to test the effects of different intersection and street features.
Accessibility Analysis of Risk Severity
Mengying Cui, University of Minnesota, Twin CitiesShow Abstract
David Levinson, University of Sydney
This study measures severity of network disruptions in the Minneapolis - St. Paul region by comparing the cumulative opportunity accessibility before-and-after removing freeway segments. Accessibility to jobs and accessibility to resident workers are measured respectively in the morning and evening peak hours. It is shown that the links with more severe consequences of disruption tend to be near or at freeway interchanges. Betweenness helps explain risk severity.
Using Mobile Device Tracking Data to Identify Trip Characteristics: A Case Study Application from MetroPlan Orlando
Jorge Barrios, Kittelson & Associates, Inc. (KAI)Show Abstract
Burak Cesme, Kittelson & Associates, Inc. (KAI)
One of the most common large datasets available for transportation planning is mobile device tracking data (MDTD). Although these data have been available for a few years, relatively little is known about its applicability at the regional, systems planning level. This paper aims to inform the reader of what the data are, how it can be presented, and how it can be applied to inform decisions at a regional level. It is based on work done for MetroPlan Orlando, the Metropolitan Planning Organization (MPO) for a three-county area in Central Florida.
MDTD can be readily used to inform on origin-destination travel patterns. In fact, most MDTD vendors offer this product. However, this study found that fusing MDTD with other readily available datasets—such as travel times and distances from routing engines—significantly increases the value of MDTD for regional planning. For example, by matching the number of trips between a given origin-destination pair with the approximate travel distance and travel time between those two points, the team was able to compare how different subregions and activity centers contribute to travel in the MetroPlan Orlando region. A similar process helped MetroPlan Orlando identify the frequencies of trips of varying length and duration—presented using histograms. In short, the primary conclusion of this study is that MDTD can be used as a cost-effective tool for region-wide planning.
Fusing Long- and Short-Distance Travel in the Colorado Statewide Model
Jeffrey Newman, Cambridge Systematics, Inc.Show Abstract
Erik Sabina, Colorado Department of Transportation
David Kurth, Cambridge Systematics, Inc.
Jason Lemp, Cambridge Systematics, Inc.
Thomas Rossi, Cambridge Systematics, Inc.
Statewide travel modeling for larger states has in most instances been undertaken as a fusion of two travel models: a “typical” daily travel model that includes shorter and more frequent trips, plus an "atypical" travel model that includes long distance trips that are undertaken infrequently. In developing a statewide model for Colorado, we have adopted an alternative approach: fusing both typical and atypical travel into a single activity-based model of travel behaviors. This approach was adopted for several reasons, including the fact that imposing any reasonable distance-based cutoff to differentiate these two models would move substantial portions of regular daily travel into the long-distance realm. Two important challenges arise from building this kind of model using a traditional household travel survey: the proper handling of non-closed tours (e.g. overnight travel) in the daily activity diary survey, and the fusion of single-day diary household survey data with longer period long distance survey data. This paper describes the methodology adopted for explicitly modeling non-closed tours, and for fusing daily diary and long distance log data into a single modeling framework.
Incorporating Big Data in an Activity-Based Travel Model: The Chattanooga Experience
Vincent Bernardin, RSGShow Abstract
John Bowman, Bowman Research and Consulting
Mark Bradley, RSG
Jason Chen, RSG
Nazneen Ferdous, CH2MHILL
Yuen Lee, Chattanooga-Hamilton County Regional Planning Commission
Passively collected anonymous cell-phone based
origin-destination data was incorporated in the spatial choice models of an
activity-based modeling system developed for the Chattanooga-Hamilton
County-North Georgia Transportation Planning Organization. This is
believed to be the first time such “big data” has been incorporated in an
activity-based modeling system. The process used the cell-phone based data
in conjunction with data on commuting flows from the Census Bureau to develop
district level origin-destination constants for inclusion in the utility
functions of the spatial choice models. In this initial application, the
constants were developed iteratively using shadow pricing techniques by
minimizing error versus the big data sources, holding fixed the other utility
function parameters originally estimated from household survey data. The
process successfully significantly improved the ability of the spatial choice
models to reproduce the travel patterns observed in the big data and contributed
to good overall model validation against traffic counts and transit
ridership. The resulting model combines the accuracy of big data and the
sensitivity of activity-based models to produce a travel model that is both
grounded in a rich behavioral framework and data driven, leveraging the large
sample size and relative completeness of spatial big data.
A German Passenger Car and Heavy Vehicle Stock Model: Toward an Autonomous Vehicle Fleet
Martin Hartmann, Karlsruhe Institute of Technology (KIT)Show Abstract
Peter Vortisch, Karlsruhe Institute of Technology (KIT)
Automated vehicles are becoming a reality with many pilot projects testing and demonstrating the technology capabilities as public authorities are allowing testing automated vehicles in real traffic. From the governmental perspective, fiscal and regulatory policies aimed at smooth transition from the conventional to the automated vehicle fleet have to be developed. But what market shares of automated vehicles will be reached when and what technology will be on board in a random sample of vehicles on a German freeway, let´s say in 2035? A national vehicle stock model acts as an instrument to answer these questions and to observe the impacts of policies influencing individual vehicle purchase decision on an aggregated level. In this paper, we present a passenger car and heavy vehicle stock cohort model oriented towards forecasting the automation technology diffusion in Germany. We developed the model using national data on vehicle stock and vehicle utilization patterns on the German freeways. The vehicle stock cohort model yielded market shares of generic automation levels in predefined instances of a trend scenario. The model results indicated market saturation of automated vehicles beyond 2050, however with almost 90% of the passenger car fleet classified as at least partially automatized by 2050. The model results also suggested faster pace of the technology diffusion within the heavy vehicle fleet than the passenger car stock, imputing, among others, a positive impact of the heavy vehicle toll on the freeway network on a fast renewal of the heavy vehicle fleet. The forecasted shares of automated vehicles can be used as an input for traffic flow simulation or as a basis for infrastructure measures and traffic policies, sensitive to the share of automated vehicles.
Comprehensive Plug-and-Play Methodology for Multimodal Travel Trend Analysis at a Metropolitan Level Utilizing Only Public Domain Data
Bo Peng, University of Maryland, College ParkShow Abstract
Yixuan Pan, University of Maryland, College Park
Shanjiang Zhu, George Mason University
Minha Lee, University of Maryland, College Park
Weiyi Zhou, University of Maryland, College Park
Lei Zhang, University of Maryland, College Park
Travel behavior data enable the understanding of why, how, and when people travel, and play a critical role in travel trend monitoring, transportation planning, and policy decision support. Departments of Transportation (DOTs) at both federal and state levels have strategically invested in travel behavior information gathering. While the estimation of travel trends plays a critical role in different aspects of urban development and traffic monitoring, the potential of public domain data lacks significant study. With decision makers increasingly requesting recent and up-to-date information on travel trends, establishing a sustainable and timely travel monitoring program based on available data sources from the public domain is in order. In this paper, a package of comprehensive methods that utilize all data accessible to the public is developed. This package can be applied to disaggregate state level traffic monitoring data into metropolitan statistical areas to understand the traffic pattern dynamically. Additionally, a case study of Seattle MSA is presented as a demonstration of the reliability and accuracy of the proposed methods.
Measuring Access to Health Care and Education by Car and Public Transport in 18 Cities Across the World
Dimitrios Papaioannou, International Transport Forum at the OECDShow Abstract
Wagner Nicolas, International Transport Forum at the OECD
This paper showcases an approach to measure accessibility to healthcare and education amenities by public transport and car in 18 cities from all over the world. To reach this goal, crowd-sourced and open-source data such as OpenStreetMaps and Global Transit Feeds are used to extract the location of amenities and compute travel times to said locations. This study develops location based accessibility indicators, measuring access to the closest school and hospital weighted by population. Past studies that develop similar accessibility indicators focus on a specific city or region, specializing the developed indicator and preventing cross city comparisons.
The obtained results are additionally decomposed to the two key elements of accessibility, land-use and transport efficiency, in an effort to understand the impact of each in the indicator. The results show that land use has a bigger effect on accessibility, since cities with higher amenity density tend to perform better, but transport efficiency is also important. Overall big European cities outscore cities from other regions of the world, especially when it comes to access by public transport.
Active Transportation Planning Beyond the Greenbelt: Key Challenges and Opportunities to Active Transportation Planning in Smaller Municipalities
Danielle Culp, Ryerson UniversityShow Abstract
Neil Loewen, Urban Strategies
Raktim Mitra, Ryerson University
Nancy Smith Lea, Toronto Centre for Active Transportation
North American communities have seen an increased popularity and a greater awareness of the benefits of active transportation modes in recent decades. Perhaps as a result, significant policy and planning measures have been taken in various urban regions to improve existing active transportation infrastructure and through those, promote and enable walking and cycling. While much is known about the active transportation planning practice and related challenges in the urban context, smaller and rural municipalities often remain overlooked. Through conducting a literature review, surveys, and telephone interviews with planning staff and key informants at 13 municipalities within the Greater Golden Horseshoe Region (GGH) in Ontario, Canada, this research examines the key challenges and opportunities that rural and smaller communities in the ‘Outer Ring’ of the GGH experience in relation to implementing active transportation infrastructure. The findings show that rural municipalities experience six overarching challenges when it comes to implementing active transportation infrastructure. They are: resources; public support; liability; design; environments; and authority. Most Outer Ring municipalities have active transportation plans, and political and professional support toward improving active transportation infrastructure. This paper also highlights approaches that were adopted to overcome some of the abovementioned challenges. The findings from this research are expected to help municipal planners, politicians and community-based organizations in learning about current practice and approaches in the rural setting, and can be used as a guide to support the development of active transportation infrastructure and complete communities in these regions.
Charter School Trip Generation
Jessica Jombai, Gresham, Smith & PartnersShow Abstract
Michael Dorweiler, Gresham, Smith & Partners
David Skrelunas, Florida Department of Transportation
This study assesses the transportation impacts associated with charter schools in the Tampa Bay region. The Institute of Transportation Engineers (ITE)Trip Generation Manual does not provide information for charter school land uses. This, together with the propagation of these types of developments in Florida, warrants further study into the impact of charter schools on the roadway system. The vehicular trip rates from this study are compared with standard ITE trip rates associated with private schools (K-8), public elementary and middle/junior high schools. Related charter school traffic impact studies imply that these up-and-coming developments might have trip generation characteristics different from those educational institutions already included in the ITE manual, and that using existing trip generation rates will underestimate the true impact of charter schools. This report is purposed to establish local trip generation rates for charter school land uses.
Background data was gathered to aid in selecting the sites for the study. A total of 10 study locations were chosen. All study locations were devoted to school use only, with no other non-school use within the building or parking areas. In selecting study locations, an effort was made to limit variation in site characteristics to provide a consistent gauge for trip generation. Traffic circulation was also observed and documented for each study location during student drop-off and pick-up times. As charter schools are becoming more prevalent, future studies with more sites are recommended to increase the reliability of the data and observations drawn from continued trip generation studies.
Evaluating the Ability of Transit Direct Ridership Models to Forecast Medium-Term Ridership Changes: Evidence from San Francisco
Alex Mucci, University of KentuckyShow Abstract
Gregory Erhardt, University of Kentucky
Transit direct ridership models (DRMs) are commonly used both for descriptive analysis and for forecasting, but are rarely evaluated for their prediction ability beyond the estimation data set. This research does so, using two DRMs estimated for rail and bus ridership in San Francisco. The models are estimated from 2009 data, applied to predict 2016 conditions, and compared to actual 2016 ridership. Over this period in San Francisco, observed rail ridership increased by 9% while observed bus ridership decreased by 13%.
The results show that the models predict 2016 ridership about as well as 2009. The models correctly predict the direction of change for both bus and rail, but underestimate the magnitude of change, predicting a 3% increase in rail and 4% decrease in bus.
A series of sensitivity tests are conducted to better understand the factors driving the ridership changes. These tests produce reasonable rail sensitivities, but reveal that the bus model is too sensitive to frequency, potentially due to the difficulty of estimating the coefficient from cross-sectional data where high-frequency transit also occurs in high-density locations.
As the travel forecasting community increases its focus on empirically evaluating forecasts beyond a base year, DRMs must be a part of that
Characterizing Activity Patterns Using Co-Clustering and User-Activity Network
Ali Arian, University of ArizonaShow Abstract
Alireza Ermagun, Northwestern University
Yi-Chang Chiu, University of Arizona
Traditionally human mobility patterns and space activities are studied using recall-based travel diaries. Following the ubiquity of location-based technologies, transportation researchers are revisiting the methods of classifying travel activity patterns using geo-location data. The current study contributes to this research line by leveraging granular and detailed activity information and building individual lifestyle patterns. We used 300 days of 402 Metropia navigation app users’ origin-destination information to construct an activity-user network. Using the co-clustering method, we discovered 16 distinct clusters or lifestyles in the dataset. The results of this study indicate: (1) Clustering individuals contingent upon their similar and dissimilar activities enables us to detect their lifestyle, (2) aggregating the activity space of individuals may misrepresent their lifestyles, and consequently mislead the policies, (3) clustering individuals contingent upon their similar and dissimilar activities has the potential to extract the demographic characteristics of individuals, and (4) understanding the mobility patterns of individuals allows us to explore and even possibly create social relationships, and thereby give them an opportunity to share their mobility. Moreover, the resulting social structures of this method can be leveraged to form and empower connected and smart communities.
Development of a Multiscale Slow-Speed Network Strategy
Ji Luo, University of California, RiversideShow Abstract
Charu Kukreja, DCR Design
Kati Rubinyi, Civic Projects
Roland Hansson, DCR Design
David Figueroa, DCR Design
This strategic plan focuses on developing a multi-scale network for slow-speed transportation users (<=25 miles per hour). The slow mode options include sidewalk modes (0-12.5 mph) and on-street rolling modes with speeds ranging from 12.5-25 mph. The slow-speed network development consists of the three interconnected networks: Slow Zones (local network), Sub-regional Network, and Slow Mode Freeways (regional network). With special attention to Complete Street policies and compliance with Active Transportation Plan recommendations, the methodology aims at characterizing the streets and land use and applying GIS-based analysis techniques to detail the slow-mode infrastructures and their attributes. The method was thoroughly applied to the historical South Bay area in Los Angeles metropolitan region, and a multi-scale slow-speed network was designed as a real-world case study. Network-wide emissions benefits are estimated as a result of increased slow modes in year 2025, and the benefits could be significant given a well-implemented slow-speed network plan.
Regional Strategic Planning Model and Development of a Multimodal Travel Demand Module
Liming Wang, Transportation Research and Education ConsortiumShow Abstract
Brian Gregor, Oregon Systems Analytics LLC
Tara Weidner, Oregon Department of Transportation
Anthony Knudson, Oregon Department of Transportation
Integrated land use and transportation models have evolved along a spectrum with simplistic sketch planning models on one end and sophisticated microsimulation models on the other. While each type of these models has its niche, they are largely unable to balance the flexibility and realism of microsimulation and the speed and interactiveness of simple models. The Regional Strategic Planning Model (RSPM) aims to fill this gap by taking a microsimulation approach but making other simplifications, to model first order long-term outcomes of land use and transportation quickly. It takes into consideration the underlying uncertainties of long-term modeling by accepting a broad range of policy inputs and technology assumptions while allowing rapid simulations of hundreds of scenarios. The RSPM is one of a few operational modeling packages (along with EERPAT and RPAT) that have evolved from GreenSTEP, a microsimulation modeling package for state-level evaluation of strategies for reducing transportation energy consumption and greenhouse gas (GHG) emissions. Several ongoing projects are aiming to develop a common software framework for the family of strategic modeling tools and improve the policy sensitivity of multi-modal travel. In this paper, we introduce the RSPM framework, and then primarily focus on the new development of a multi-modal travel demand module that links various policy inputs to households’ multi-modal travel and further to aggregate transportation outcomes (e.g. GHG emissions, traffic fatalities). We discuss our choice of model structures and specifications and then estimate the models utilizing a unique US nationwide dataset combining the 2009 US National Household Travel Survey (NHTS), EPA’s Smart Location Database, and the National Transit Database. This comprehensive dataset provides a rich set of variables capturing household social-demographics, multi-modal travel, built environment, and transportation supply. We conclude the paper with the results of validation and sensitivity tests, and a discussion of future work.
Optimizing the Evaluation of the Life-Cycle Impacts of Intersection Control Type Selection
Joy Davis, Institute for Transportation Research and Education
Using Linked Nonhome-Based Trips in Virginia
Nobody Knows Who We Are: Celebrating 50 Years
Jacob Gonzalez, Benton Franklin Council of Governments
Edmonton Valley Line LRT Traffic Modeling
Michael Mahut, INRO Consultants, Inc.
Modeling Ridesharing Within State-of-the-Art Travel Demand Models
Klaus Noekel, PTV Group