Travel Demand Model Research Articles

Transportation planning using activity-based travel demand model

According to Bangalore's Transportation Department, the number of transport vehicles in the city will be close to 85.6 lakh two wheelers, 50 lakh automobiles, and 20 lakh transport vehicles by the end of 2023. Two-wheelers account for 58.41% of all vehicles, while Light Motor Vehicles (LMVs) account for 22%, Heavy Motor Vehicle (HMVs) for 11%, and other vehicles account for 8.59%. Therefore, there is a need for efficient transportation planning. From earlier research, it was found that activity-based modeling is more efficient for evaluation. In this study, a method was proposed to develop an activity-based travel demand model for the selected zones of Bangalore city, and work trips were generated from the Global Tech Park. The trips were monitored, and it was found that people came from all around Bengaluru. Four zones in Bengaluru were chosen for this study because they provided the most travel to the Global Tech Park. The four zones were Kengeri, Mylasandra, RR Nagar, and Vijaynagar. The data were collected through individual person surveys, considering the influential parameters in developing person tours. The collected data were analyzed using SPSS software, and models were developed based on parameters such as age, sex, monthly income, distance traveled, active travel, daily travel cost, and vehicle ownership. The results obtained in the form of the models were compared with those of traditional models. In addition, the factors influencing the trips of each individual were studied, and the effects of these factors were analyzed. The results obtained were satisfactory in terms of the R2 value and other testing parameters. From the primary data analysis, it was found that the preference for modal choices of the public increased from public transport (i.e., buses) to private transport (i.e., owned cars) as per the increase in age, income, professions, travel distance, and daily travel cost.

Analyzing Potential Impacts of Motorcycle Travel Demand Management Using an Activity-Based Travel Demand Model for Ho Chi Minh City, Vietnam

This paper analyses the potential impacts of motorcycle travel demand management strategies for Ho Chi Minh city, a motorcycle dominant city of Vietnam. Four scenarios on managing the use of motorcycle are analyzed using an activity-based travel demand model called FEATHERS-HCMC. The model is adapted from the FEATHERS model developed for Flanders, Belgium and based on the household travel survey in Ho Chi Minh City. The modeling framework of FEATHERS-HCMC is a combination of the heuristic rules and the econometric models. The simulation results suggest that a strong enforcement on motorcycle use would significantly reduce motorcycle trips and induce the rearrangement of location and time-of-day for daily activities. These results provide empirical evidence that shows several advantages of using activity-based travel demand models comparing to the current four-step models. FEATHERS-HCMC is a promising tool to support decision-making process for assessing sustainable transportation policies, not only for HCMC but for other regions with similar characteristics.

Refining agent-based travel demand models using social media data

This work presents an approach for detecting localisable events from social media using machine learning techniques and how this information can be used in agent-based travel demand models. The approach allows for explicitly modelling irregular mobility patterns that emerge from incidences and events that gather more people as usual in certain locations, and thus, create deviating travel demands. The advantage of our approach is that it solely relies on publicly accessible social media data that can be analysed in real-time. The method is demonstrated along a case study on mapping the event-related mobility scene in Berlin (Germany).

The Impact of Socio-Economic, Land Use, and Travel Related Variables on Escort and Non-Escort Intermediate Stops on Work Tours

Stop frequency prediction model is one of the components of the activity-based travel demand models. Most of the previous studies have considered stops during commutes regardless of their purposes. This approach does not yield the contribution of the explanatory variables to the likelihood of making stops of different purposes. Besides, most of the former studies have been conducted in larger metropolitan areas. This study attempts to cover these gaps by using 2012 travel data of Fargo-Moorhead medium-sized US metropolitan area and classifying stops on work tours into escort, non-escort, and a combination of all stops. The results of logit models indicate that personal characteristics of the commuters do not contribute to the escort stop participation likelihood. In addition, household size variables have a large impact on the likelihood of participating in escort stops and participating in the combined stops on the outbound leg of the commutes. Contrary to several previous studies, the significance and sign of the coefficient of income level vary for different stop purposes. Commuters seemed to be more likely to make more than one non-escort stop close to their workplace on the outbound legs of their commutes. The general results suggest separating the stop purposes yields more illustrative results rather than using one model for the combined stops.

Analysis in the field of volume-delay function research

One of the key parts of the travel demand model is the correct determination of the performance of the road system. Performance is partially determined by the correct choice of volume-delay functions (VDFs) and their input parameters. Worldwide, transport modellers have a wide range of VDFs to choose from, but research is needed to select the one that provides the most accurate estimates for specific conditions. The problem is that road conditions and traffic conditions are specific to each country, so assuming that we can use VDFs and their parameters based on traffic data from another country can lead to errors. For this reason, there is a need for research on VDFs in the context of a particular country's traffic conditions.The main objective of this paper is to provide knowledge and practices that can be used as a basis for future research. This paper discusses the most commonly used VDFs for links, their parameterisation and the types of data required for calibration, and approaches to their collection. This paper also aims to introduce crowdsourced real-time data as a possible new source of traffic data, with a focus on traffic data from the Google Maps Directions API. The results of this paper will be used in future research to select and parameterise the best-fitting VDFs for specific types of road facilities and traffic conditions within the national transport model of the Slovak Republic.

Urban transport energy demand model for Riyadh: methodology and a preliminary analysis

ABSTRACT Saudi Arabia intends to reduce GHG emissions by 278 million tons of CO2eq annually by 2030 through Nationally Determined Contribution (NDC) to UNFCCC. Among many policies, mass transit system and transit-oriented development are being developed with the expectation to reduce energy consumption and GHG emissions in Riyadh. To what extent such initiative can reduce energy consumption and GHG emission is an important question. In this paper, a methodology to systematically measure the impact of mass transit and transit-oriented development in Riyadh city on the energy demand has been developed. For Riyadh, a comprehensive travel demand model considering the impact of mass transit and transit-oriented development is still missing. To this end, this paper aims to fill the gap. This methodology considers the state-of-the-art in travel demand analysis and the local context by combining traditional four-step model and activity-based model for modal-shift. This paper describes the methodology and its application for Riyadh by analyzing modal-shift only between car and metro. The results suggest that metro can reduce energy consumption, but the reduction varies with varying accessibility, car, and metro situations. At high urban density and higher car travel cost, we may achieve as high as 13% reduction in fuel demand.

Sustainable mobility strategies and their impact: a case study using a multimodal activity based model

Nowadays, many cities are intending to reduce the use of private vehicles. Governments are incorporating new mobility services and are adapting their parking policies to promote a more sustainable mobility, as both strategies are believed to have the potential to reduce private vehicle use. To understand the effects of these strategies, one needs to be able to model complex travel behaviour up to a very high level of detail. Owing to their flexibility, robustness and ability to model travel activity behaviour on an individual level, activity based travel demand models (ABM) offer a highly suitable methodology for this purpose.In this paper, we employ this methodology to perform a case study in a metropolitan region in the Netherlands which surrounds and includes the cities of Rotterdam and The Hague. This region is of vital economic importance and has a very developed and dense road network. The population of this region is growing, which motivates the ambition to improve its accessibility and move towards sustainable mobility. Therefore, the findings of this study are important to similar regions seeking to do this as well. After setting up a suitable, calibrated ABM able to perform a comprehensive study on the effects of new mobility services and parking policy adaptations in the above-mentioned region, we design seven scenarios to give quantitative answers to policy-related questions on how altering features can reduce the extent to which private vehicles are used for travelling. These features include the availability of mobility hubs (hubs on neighbourhood level where sustainable travel modes are linked), the availability of car/bike sharing services, the availability of ‘Mobility as a Service’ (MaaS) subscriptions, the amount of parking capacity in the region and the parking costs. We also study what the impact would be of an improved public transport service with lowered public transport travel times to and from the city centers, and the impact of an improved cycling network infrastructure with significantly lowered travel times for bike and e-bike travellers.Based on the case study, we find that the introduction of mobility hubs alone has limited impact. However, combining this with making sharing services available to the public through MaaS subscriptions, there is a potential to reduce the number of car trips significantly, while the number of trips undertaken by a more sustainable (shared) e-bike increases as well as the number of so-called multi-modal mode trips (trips undertaken by a combination of various modes). Furthermore, improving the public transport service and micromobility network further increases the potential of mobility hubs in terms of making mobility more sustainable. The case study also shows that limiting parking capacity and increasing parking costs in the city centers is especially helpful for the reduction of vehicle use, leading to an improved car flow.

COVID-19 impacts on mobility and travel demand

Since the beginning of the COVID-19 pandemic, many travel restriction policies were implemented to reduce further spread of the virus. These measures significantly affected travel demand to levels which could not have been anticipated by most planners in transportation agencies. As the pandemic has proven to have significant short-term impacts, it is anticipated that some of these impacts may translate to longer-term impacts on overall travel behavior and the movement of people and goods. Beyond the pandemic, the observed travel patterns during this period also provides a great opportunity for planners to assess policies such as work from home and remote learning as strategies to manage travel demand. This study provides a scenario analysis framework to re-evaluate travel demand forecasts under uncertain future conditions using the Maryland Statewide Transportation Model (MSTM). Model parameters associated with working from home, household income, changes in discretionary travel, distance learning, increased e-commerce, vehicle occupancy and mode choice were identified. Parameter values were assigned under the various scenarios using employer surveys on workforce teleworking and observed data on e-commerce growth and shopping behavior. The main findings of this study capture the sensitivities of systemwide vehicle miles travel, and vehicle hours travel under different scenarios and implications on future investment decisions.The study found that future investments under the scenarios remain beneficial to systemwide performance and therefore justified. Although this study focuses on the state of Maryland, the scenario framework and parameter definitions can be used in other states or agencies within a travel demand model environment.

Evaluating the impact of private automated vehicles on activity-based accessibility in Japanese regional areas: A case study of Gunma Prefecture

Automated Vehicles (AVs) are expected to disrupt the transport sector in the mid- to long-term future, and extensive research efforts have been dedicated to studying its potential implications. However, the existing literature is still limited in the context of Japan. To fill this gap, an integrated travel forecasting approach that combines an activity-based travel demand model and dynamic traffic assignment is estimated and validated to evaluate the impacts of AV-induced travel, and the feedback effects between transport supply and demand. The target area of this study is Gunma prefecture, a regional area in Japan. The planning horizon is set to 2040, where the effects of population decrease are reflected in the scenario settings. Policy scenario analysis that assumes complete substitution of human-driven vehicles with AVs suggests increases between 22.1% and 43.6% in total distances traveled, with a corresponding increase in increased CO2 emissions between 23.6% and 46.0%. While this suggests a worsening network level of service, around 2 to 3 min gains in median activity-based accessibility were also observed, suggesting overall positive effects from AV adoption in the study region.

Action plans on the reduction of mobility energy consumption based on personal mobility activation

Personal Mobility (PM) has attracted considerable attentions as an eco-friendly mode of transportation. In Seoul, the PM penetration rate has grown by 30% annually, and the number of PM companies has been increasing. However, there is a high risk of collision against a pedestrian while riding PM vehicles on sidewalks. To create a safer operation environment, studies on the effect of PM-only lane and ways to create PM playgrounds have been conducted. A design involving a PM-only lane was presented and evaluated using the traditional travel demand modeling framework. If the PM-only lane is to be implemented, a significant number of vehicles would be converted for public transportation, and vehicle kilometers traveled would be substantially decreased. Reducing the use of automobiles is expected to contribute to environmental benefits. Moreover, by creating a PM-based playground, it can promote a safer driving environment through driving education for beginners, and data collection related to PM driving. If PM-only lane and PM-based playgrounds positively affect the deployment of PM, they could lead to the activation of public transportation uses. Environmental benefits calculated on these action plans show its effectiveness of reduction of mobility energy consumption while ensuring safety and convenience.

Mode choice of city tourists: Discrete choice modeling based on survey data from a major German city

With growing city tourism, there is an increasing need for urban travel demand models to consider traffic generated by visitors. Existing research has concentrated on socio-demographic and journey-related factors to determine what influences the mode choice of tourists. In contrast, revealed preference data, such as travel time, is almost never considered. In this article, we present the results of discrete choice modeling of city tourists’ mode choice based on revealed preference data from a survey we conducted in Kassel, Germany. We used multinomial logit models and determined the model parameters using maximum likelihood estimations. Surprisingly, travel time played a smaller role in mode choice than assumed from previously established understanding about everyday mobility. In the final model, travel time was only significant for the alternative of walking. Also, most other sociodemographic and journey-related variables showed no significant influence. The final model reproduced the mode choice, but the goodness of fit was lower than expected from other research. We conclude that modeling the travel behavior of tourists is more complex than everyday mobility. An alternative approach that we suggest would be to model trip chains rather than single trips.

Trips Production in Khartoum State

The rapid increase in population, fast urbanization and change, high demand for public transport in Khartoum State necessitates development of a proper transportation plan for the state, to enable predicting the impacts that various policies and programs will have on travel. Travel demand forecasting is an essential part of the transportation planning process and trip production is the first step in the concept of four steps travel demand model. In this paper a model for trip production in Khartoum State is proposed. The trip generation model is based on data collected by the Ministry of Infrastructures and Transportation and a consultant in the period 2008-2011. The exclusion of incomplete data and outliers and the consideration of the car ownership in the development of the model, has resulted in a more realistic estimate of the number of trips produced than those predicted by the currently adopted model of Khartoum State. The results of this study predict the number of daily trips in the year 2035 at 9.56 million person-trips, creating a demand of 6.69 million person-trip/days to be covered by public transport. This will necessitate huge investments in mass transit systems to accommodate the uprising demand.

Implementing a Ride-sharing Algorithm in the German National Transport Model (DEMO)

As macroscopic travel demand models are widespread among regional modelers, it is crucial to develop extensions for new mobility services such as ride-sharing services to assess their effects on the transport system. In a feasible approach that was introduced with previous research, origin-destination network-based paths are transformed to a series of crossed traffic analysis zones (TAZs). These series are compared to find overlapping trajectories to derive ride-sharing potential. However, in national transport models the TAZ sizes are larger than in regional models, which can lead to unrealistically high matching rates because of imprecise path trajectories. This paper presents a modified approach for implementation in the German national transport model, DEMO. The main adjustment is to use population centers as pick-up locations instead of TAZs. Each TAZ includes multiple points. This way, the trajectories of paths can be better differentiated. Another advantage of this method is that intrazonal trips can be distributed between the centers and thus be included in the algorithm. With the successful implementation in DEMO, the effects of new mobility tools in German cities can be evaluated within the same model.

The effect of a smart mobility hub based on concepts of metabolism and retrofitting

Many countries, including Korea, are striving towards carbon-neutrality by 2050. Accordingly, this study proposes a method for cutting carbon emissions by reducing the use of passenger cars through a travel demand management scheme called mobility hub. The effectiveness of this approach has been estimated by designing a mobility hub for Seoul using a location plan and mobility design. In particular, some locations of area widely scattered transport facilities, including bus stops, subways, public parking lots, and gas stations have been selected and adjusted to design mobility hub in order to enhance mobility metabolism. A design involving smart mobility and retrofitting of the existing facilities towards the mobility hub has been presented and evaluated in the traditional travel demand modeling framework. If the mobility hub is to be implemented, a significant number of vehicles would be converted to public transportation, and vehicle kilometers traveled would be also decreased substantially. The resulting decrease in carbon emissions would contribute around two percentage reduction by transport sector in the Seoul area in the initial stage. Thanks to the design of the mobility hub, it is effective not only for the environmental benefits by modal shifts to public transportation, but also for health promotion by changing people's travel behavior.

Effective Modeling for a Distance-Based Fare Structure with a Time-Expanded Network

This study addresses the appropriateness of a passenger assignment model for a distance-based fare structure that recently has received attention from transit agencies, along with technological advances in electronic fare payment and global positioning systems. Among the two major passenger assignment models, the study identifies that the schedule-based model has an advantage over the frequency-based model for representing a distance-based fare structure. The schedule-based model that explicitly traces passenger routes enables the accurate calculation of a distance-based fare and its variant. In addition, the study addressed the implementation issues associated with the schedule-based model. As the schedule-based model is considered to require more data than the widely-used frequency-based model, the study suggests a way to mitigate this data requirement issue by approximating the timetable at each stop with the same data used for the implementation of the frequency-based model. A case study on the field application of the schedule-based model and its availability as a ready-to-use option in most commercial software for travel demand modeling is described.

Comparative Analysis of AERMOD-Predicted Pollutant Concentrations by Input Source Type in Atlanta, GA

This research investigates the differences in AERMOD-predicted pollutant concentrations across input source types (AREAPOLY, VOLUME, LINE, RLINE, and RLINEXT without barriers). AERMOD (version 19191) microscale dispersion modeling is performed for the Atlanta I-75/I-575 Northwest Corridor (NWC) subarea in the U.S., including freeway corridors, managed lanes, connecting arterials, and intersections serving the NWC system. Modeling is performed for all link-receptor pairs, for each hour of the year, using the approved AERMET meteorological files (24 h × 365 days in 2019) from Georgia Environmental Protection Division (GA EPD), and emissions rates are estimated using MOVES-Matrix, based on predicted speed and volume by hour (output from ARC’s ABM2020 travel demand model). The AERMOD comparative analyses are performed for the same receptor sets, link-level volumes and speeds, and fleet compositions, across the five source types to generate comparative model results by input source type for the subarea. Results indicate concentrations from RLINEXT and RLINE were similar, with differences smaller than 0.05%, and their results were significantly higher than other source types. The results from AREAPOLY were similar to those from LINE (both smaller than RLINE and RLINEXT results). It is indicated that the results from VOLUME are systematically different from AREAPOLY and LINE for both downwind (much lower than AREAPOLY and LINE) and upwind (much higher than AREAPOLY and LINE) of the sources, and the sensitivity analysis indicates these predictions occur when wind speeds are low (less than approximately 2.5 m/s), which is because the enhanced wind meander approach employed with VOLUME is different from other source types.

An Advanced Travel Demand Synthesis Process for Creating a MATSim Activity Model: The Case of Ústí nad Labem

In this study, an activity-based travel demand model of the Ústí nad Labem district (Czech Republic) is created. To do this, an advanced travel demand synthesis process is presented by utilizing the Eqasim framework, which is a pipeline-processing, initial raw data to simulation step. The framework is extensively modified and extended with several algorithms in order to utilize multiple data points for increasing realism in mobility for travel demand models. Two major extensions are provided. First, the pipeline framework is improved to estimate inbound and outbound trips of the study area, comprising a main city and 23 surrounding municipalities. The extended framework assigns synthetic gates for the study area as hubs for the inclusion of inbound and outbound trips. Second, the pipeline framework is advanced to provide a more compatible match of travel destination and activity location state. To do this, the extended framework assigns a capacity for each facility identified for the study area, the expected number of visitors to each facility, and the number of residents in each building. The resulting demand model is presented and the generated trips are evaluated based on locational, transport mode, and sociodemographic characteristics with origin–destination (OD) bundling. Additionally, distribution analyses of the present model are conducted to understand the matching results on a detailed level. The results demonstrate that the present model provides a reasonable output for transport researchers when testing different mobility scenarios and the provided extensions helps them to reduce implausible reflections of the distribution of travel and activity characteristics in household travel surveys while creating demand models, thus increasing realism. Lastly, open-source playground and code repository for further future improvement of synthetic travel synthesis methods are created, which enhances a deep understanding of the preparatory and methodological backgrounds required for complex activity-based simulations in order to inspire transport planners.

Modeling county-level benzene emissions using transportation analysis zones in the Denver metro area

The link between exposure to benzene (an important toxic air pollutant) and physical health is well-documented as benzene is a known carcinogen. Understanding benzene concentration distributions in urban areas is very important and can be done using air dispersion models. An essential input requirement for dispersion models is the spatial and temporal allocation of emissions. Since most county-level benzene emissions come from gasoline powered motor vehicles, the goal of this work was to develop a new method of county-level benzene emission allocation using transportation analysis zones (TAZs) commonly used in travel demand models. The high spatial resolution provided by the TAZ allocation method helped visualize different flux patterns generated from five emission sectors and identified where more effort is needed to reduce benzene concentrations. The new allocation method was also tested using the AERMOD dispersion model to estimate benzene concentrations in the Denver metropolitan area. The model performance was assessed using 2014 morning benzene measurements from a monitoring station located in downtown Denver; results showed an acceptable model-to-monitor ratio. The model was also used to investigate the effect of temperature inversions on morning benzene concentrations. As expected, predicted benzene concentrations were higher during temperature inversions and model-to-monitor ratios during investigated days were within a factor of two. The TAZ allocation modeling methodology demonstrated in this work can improve the estimation of air pollution exposure in future health-related studies. • County-level benzene emissions were allocated based on transportation analysis zones. • This new allocation method identified on-road, non-road, and oil and gas as major emission sources. • Urban benzene concentrations were reasonably predicted using the updated emission allocations and a dispersion model. • Model-predicted and measured benzene concentrations were elevated during morning temperature inversions.

Implementation of a Danish activity-based model

Accurately modeling individual travel demand and behavior is essential for forecasting the impact ofalternative policies both at the disaggregated and aggregated levels. In the last decades, the state-ofthe-art of travel demand models advanced from traditional four-step models, passing through tourbasedmodels, to activity-based models. The latter models postulate that the demand for travel isderived from the demand for performing activities, i.e., travels are only undertaken when the utility ofan activity and its associated travel exceeds the utility of activities involving no travel [1]. Furthermore, itconsiders that daily travels are temporally and spatially constrained, and individuals start their day athome and return to a home base at the end of the day

Activity-based integrated modelling for assessing COVID-19 impacts on transport operations and emissions

ABSTRACT This study combines an integrated transport, land-use, and energy (iTLE) modeling system with traffic microsimulation model and emission simulator for a holistic analysis of COVID-19 pandemic related changes in traffic flows and emissions. An activity-based travel demand model within iTLE informs pandemic traffic operation scenarios for traffic microsimulation modeling. Link-based simulation outputs inform a finer-grained emission estimation process within a MOtor Vehicle Emission Simulator. Results suggest that the overall network performance improves during lockdown as average delays and queue time decrease by 42.04% and 5.9% respectively compared to pre-COVID condition. Emission results reveal that GHG emissions significantly decrease (64%) in lockdown while it starts increasing gradually in post-pandemic period. Link-based emission analysis indicates that major arterial streets achieve a significant reduction in air pollutant emission. The findings of this study will help transportation planners, engineers, and policymakers to devise effective policies for the improvement of transport operations and emissions.