Changes In Travel Demand Research Articles

Potential effects of automated driving on vehicle travel demand: A comparison of three case cities

Automated vehicles (AVs) hold the potential to reduce road accidents, mitigate traffic congestion, and improve travel experience. However, the possible countervailing impacts from the changes in underserved populations' vehicle travel demand tend to be overlooked. To determine the vehicle travel demand changes that resulted from underserved populations aged between 6 and 80, this paper explores the latent effect of AVs on vehicle kilometers traveled (VKT) in a fully AV environment using person trip survey data from the cities of Sanya, Shijiazhuang, and Shenzhen in China. This paper uses the natural decline hypothesis of travel demand and proposes a regression model to investigate the difference among the cities' latent vehicle travel demand. Results show that the average VKT of the overall population in Sanya, Shijiazhuang, and Shenzhen increased by 33.4%, 47.0%, and 46.8%, respectively. The analysis of the regression model confirms that the current travel behavior of individuals can affect the degree of increase in their average VKT. Integrating AVs into public transport, increasing the acceptance of automated shared mobility options, transforming road space use type, and prototyping AV designs with various features and needs are potential methods to cope with the countervailing impacts. The total VKT of the overall population increased by approximately 10%–25% depending on the city. The conclusions of this paper provide informative insights into the evaluation of VKT for underserved populations and contribute to the deployment of AVs to address equity and inclusion issues.

Logistic model for pattern inference of subway passenger flows based on fare collection and vehicle location data

With large volume of passengers boarding and alighting through subway platforms, the stations are getting crowded, resulting in drops in the level of service and safety concerns, especially for subway systems operating at capacity during peak hours. Thus, it is crucial for subway agencies to sense changes in travel demand and adjust their management schemes accordingly. In this paper we propose a statistical approach to estimate dynamic passenger flows with automated data. First, we develop a dynamic logistic model for calculating passenger tap-out times, which can be employed to infer passenger flow characteristics at the aggregate level. In addition, a new passenger-to-train assignment model for any subway route is derived based on the dynamic model. Subsequently, we apply an expectation-maximization algorithm to estimate the model parameters with automated fare collection and automated vehicle location data. Finally, a cross-validation method is employed to validate our approach with data obtained from several routes in Beijing subway system in China. Results of 95% prediction intervals indicate the effectiveness of the models and the proposed estimation methods.

Travel Demand Estimation for a Special Event using Pervasive Data: A Case Study of G20 Summit

Due to globalization and rapid development, the demand for travel and congestion are increasing, stressing the road networks. Furthermore, road networks are subjected to planned and unplanned disruptions, and these disruptions affect the road network performance by either damaging its components, fluctuating the travel demand, or doing both at the same time. Thus, assessing the change in travel demand and travel patterns during such disruptions is essential. Conventionally, travel demand has been estimated using a household travel survey (HTS). However, the HTS is not feasible for disrupted conditions because of its large sample size requirement and time-consuming methodology. However, with the advent of crowdsourced data, new methodologies have been proposed for estimating the travel demand, but most of these studies have focused on estimating demand for a typical day and not disrupted scenarios. Moreover, data sources utilized till now have low sample sizes or are unavailable for developing countries. To bridge this gap, the current study utilizes an automated planning tool called Rapidex to estimate the travel demand and pattern change for a planned disruption, i.e., the 18th G20 Summit, Delhi, using crowdsourced travel time data from TomTom API. Upon comparing the results of travel demand and travel patterns of typical days with G20 days, it was observed that travel demand was lower during G20. Moreover, it was observed that the zones where railway stations, interstate bus terminals, and G20 activities were focused had higher generation and attraction share than typical days. Other zones within the regulated area had lower generation and attraction rates, which can be because of travel restrictions imposed in these zones.

Exploring the association between travel demand changes and the built environment during the COVID-19 pandemic

This study compared the extent to which COVID-19 impacted travel demand of bike-sharing and taxi in New York City, and further explored how the changes in travel demand were associated with the built environment through four typical regression models, namely, least squares (OLS) regression, geographically weighted regression (GWR), temporally weighted regression (TWR), and geographically and temporally weighted regression (GTWR) models. In particular, this study looked at two phases: the lockdown phase (during which travel demand decreased dramatically) and initial recovery phase (during which travel demand started to increase). The results suggested that 1) GTWR performed better than the other three model types; 2) shared bike ridership rebounded much more significantly during the recovery phase than taxi ridership; 3) Commercial Point of Interest (POI) was positively associated with the change of ridership in both lockdown and recovery phases.

Near-term impact of COVID-19 pandemic on seniors’ crash size and severity

Recent research revealed that COVID-19 pandemic was associated with noticeable changes in travel demand, traffic volumes, and traffic safety measures. Despite the reduction of traffic volumes across the US, several recent studies indicated that crash rates increased across different states during COVID-19 pandemic. Although some recent studies have focused on examining the changes in traffic conditions and crash rates before and during the pandemic, not enough research has been conducted to identify risk factors to crash severity. Even the limited research addressing the contributing factors to crash severity were focused on the pool category of drivers and no insight is available regarding older drivers, one of the most vulnerable groups to traffic collision and coronavirus. Moreover, these studies investigated the early impact of the COVID-19 pandemic mostly using up to three months of data. However, near-term and long-term effects of the COVID-19 pandemic are still unknown on traffic collisions. Therefore, this study aims to contribute to the literature by studying the near-term impact of the COVID-19 pandemic on crash size and severity among older drivers. To this end, a relatively large sample of crash data with senior drivers at fault was obtained and analyzed. To identify the main contributing factors affecting crash outcomes, Exploratory Factor Analysis was conducted on a high-dimension data set to identify potential latent factors which were validated through Confirmatory Factor Analysis. After that, Structural Equation Modeling technique was performed to examine the associations among the identified independent latent factors and the dependent variable. Additionally, SEM model identified the impact of the COVID-19 pandemic on seniors’ crash severity. The findings reveal that several latent variables were the significant predictors of crash severity of older drivers including “Driving maneuver & crash location”, “Road features and traffic control devices”, “Driver condition & behavior”, “Road geometric characteristics”, “Crash time and lighting”, and “Road class” latent factors. The binary variable of “Pandemic” was found to be as highly significant as the last four latent factors mentioned above. This means not only were older drivers more likely to be involved in higher crash size with higher severity level during the pandemic period, but also “Pandemic” was a risk factor to seniors as much as “Driver condition & behavior”, “Road geometric characteristics”, “Crash time & lighting”, and “Road class” factors. The results of this study provide useful insights that may improve road safety among senior drivers during pandemic periods like COVID-19.

No way back? A survey on changes in travel demand post-pandemic in Norway

Research shows a significant reduction in travel demand during the pandemic. Vaccines are currently distributed, allowing us to go back to pre-COVID-19 life, if that is the case. This study provides insight into changes in activities and travel demand during the pandemic and expected changes after the pandemic.Our paper presents the findings from a Likert scale survey (n = 360) conducted in Norway June–July 2021. We investigate how relocated activities impacted travel behaviour during the pandemic, and prospects of lasting impacts of the pandemic.The results show that the prevalence of home office and digital meetings, the new normal for many people during the pandemic, will continue to some degree after the pandemic. There is an increased willingness to commute by active modes, while no pronounced increase in car commutes is reported. However, car use is expected to increase for leisure trips; a significant correlation with the demand of more domestic outdoor travel. One out of five states that they will use public transport less often in the future. We may expect more online shopping, although home-deliveries for groceries is not as popular as for non-edible goods—not during the pandemic nor after.Earlier disruptive events have shown that people tend to adjust back to normal after a while, even if they have stated otherwise. There are certain mid-pandemic habits that go along with political goals and/or employers’ interests, implying these might be prolonged into the future.

A multimodal multi-provider market equilibrium model: A game-theoretic approach

The prominent planning components of recent transport systems are multimodality, pricing, shared mobility, and bundling. These systems consist of several service providers competing for a higher benefit or a lower cost. This paper proposes a generalised multi-modal multi-provider market equilibrium model to evaluate the operation of transport systems. The model includes the modes of a private vehicle, walking, public transport, ride-sourcing, ridesharing-as-driver, and ridesharing-as-rider. The economic behaviours of the service providers and a network operator are modelled using optimisation problems, i.e. their Karush–Kuhn–Tucker (KKT) optimality conditions in combination with user equilibrium conditions of travellers, forming a complementarity formulation for the market at equilibrium. Extensive computational experiments on an abstract network in Sydney demonstrate the use of the model for handling responses of various market players to EVs technological improvements, travel demand changes, restrictions on GHG emissions, and diminishing ridesharing use cultural barriers.

A Statistical Analysis Arrival Times at the Airport Using Chi-Square and Box Plots Analysis

In this report we will be investigating two datasets and calculating their statistical quantities for the interarrival times of incoming flights at DFW airport. This set contains the flight arrival time information of flights between roughly 6:00 AM and 12:30 PM on September 12th, 2022. The data was collected from the website by copying, pasting, and manually formatting the text provided by it. Our team believes that flights happened in a pattern which will follow an exponential distribution when graphed. We extrapolated and formatted the data based on the given flight times and presented it in tables and graphs to better visualize the data. While doing this has not confirmed our hypothesis, it shows promising similarities to our expectations and merits further testing. The main Grand Challenge that this and other datasets take on is restoring and improving urban infrastructure. The main SDGs it serves is industry, innovation, and infrastructure, as well as sustainable cities and communities. All the datasets can be used to advance infrastructure. Improving energy efficiency can lower expenses for everyone, lower emissions, and allow for other areas of infrastructure to be taken care of. Identifying trends in inter-arrival times will inform the global infrastructure to account for changes in travel demand, allowing business, leisure, and trade, etc. to continue with minimal problems and efficient spending.

Residential relocation and changes in household vehicle ownership and travel behavior: Exploring the context of Hubli-Dharwad twin-cities in India from a planning viewpoint

• Studies residential relocations and travel behaviour. • Primary data from Hubli-Dharwad twin cities in India. • Descriptive analysis and structural equations modelling. • Variations in the impact of built environment on travel behaviour. • Travel attitude is not related to self-section. • Self-selection effect due to gender varies between cities. This article simultaneously analyzes residential relocation, vehicle ownership changes, and travel demand changes while accommodating potential self-selection effects to unravel the causal impacts of built environment features on vehicle ownership and travel behavior. Earlier studies on residential mobility have not considered the causal link from objective built environment features to subjective built environment, and rarely control for attitudes and objective built environment features in the empirical analysis. Besides, the literature has not discussed how households perceive neighborhood in terms of children's/women’s travel and how that changes by residential relocation and impact mobility decisions. The paper compares built environment impacts on mobility decisions in two adjacent areas in India, Hubli-Dharwad twin cities, that vary by socio-economic and land use contexts. The study notes that Dharwad residents acquired more personal vehicles than Hubli residents, while the proportion of households who relinquished their vehicles is high in Hubli. Post movement, Dharwad residents opted for areas with higher diversity and density than their counterparts in Hubli. In Hubli, households find that their current location does not help their children use active modes to schools, and women find jobs closer to home. The statistical analysis revealed that Hubli residents prefer locations with improved travel conveniences for women and children. With improvements in the children's travel friendliness, households of both cities decreased vehicle ownership, whereas household vehicle ownership of both cities increased with improvements in women’s travel friendliness. In Dharwad, moving to residential areas characterized by high land-use diversity reduces commute travel, while an inverse association is noted in Hubli. The statistical analysis did not confirm self-selection effects due to travel attitudes. In Dharwad, the latent factor on women’s travel friendliness featured in self-selection and shows an independent effect on vehicle ownership. In Hubli, the factor did not have independent effects on vehicle ownership after accommodating travel attitudes and socio-demographics.

The Impact of COVID-19 on Travel Mode Choice Behavior in Terms of Shared Mobility: A Case Study in Beijing, China.

Shared mobility is growing rapidly and changing the mobility landscape. The COVID-19 pandemic has complicated travel mode choice behavior in terms of shared mobility, but the evidence on this impact is limited. To fill this gap, this paper first designs a stated preference survey to collect mode choice data before and during the pandemic. Different shared mobility services are considered, including ride hailing, ride sharing, car sharing, and bike sharing. Then, latent class analysis is used to divide the population in terms of their attitudes toward shared mobility. Nested logit models are applied to compare travel mode choice behavior during the two periods. The results suggest that shared mobility has the potential to avoid the high transmission risk of public transport and alleviate the intensity of private car use in the COVID-19 context, but this is limited by anxiety about shared spaces. As the perceived severity of the pandemic increases, preference for ride hailing and ride sharing decreases, and a price discount for ride hailing is more effective than that for ride sharing at maintaining the ridership despite the impact of COVID-19. These findings contribute to understanding the change in travel demand and developing appropriate strategies for shared mobility services to adapt to the pandemic.

The Analysis of the Effects of a Fare Free Public Transport Travel Demand Based on E-Ticketing

The traditional approach in public transport planning was to collect travel demand data for a more extended period and compose timetables to serve this demand. There are two significant identifiable issues. In the rural areas and off-peak hours, public transport operators provide much more capacities than needed. On the other hand, more capacities than scheduled are needed on certain lines at certain departures on some sporadically occurring occasions. The problem is how to react to short-term changes (daily) triggered by exceptional circumstances and events and midterm changes (weekly, monthly basis) in travel demand. We can trigger changes in travel demand chiefly by introducing a desirable (almost for free) tariff system applied to specific populations. No long-term travel response data exists for this kind of intervention, but an immediate response in public transport supply is needed. In Slovenia, public transport for free for the whole population over 65 years was introduced. With the modern ticketing system, which was designed to be as simple as possible for users (that means »check-in only« at the moment of boarding), the research task was to analyze the travel behavior of the retired population, faced with a new attractive option to travel, based on data of purchased tickets and their afterward validation, for better mid-and long-term planning. Our study finds that ITS technology (in this case, e-ticketing system) can satisfactorily solve the discussed planning and management task.

Effect of the COVID-19 pandemic on bike-sharing demand and hire time: Evidence from Santander Cycles in London.

The COVID-19 pandemic has been influencing travel behaviour in many urban areas around the world since the beginning of 2020. As a consequence, bike-sharing schemes have been affected—partly due to the change in travel demand and behaviour as well as a shift from public transit. This study estimates the varying effect of the COVID-19 pandemic on the London bike-sharing system (Santander Cycles) over the period March–December 2020. We employed a Bayesian second-order random walk time-series model to account for temporal correlation in the data. We compared the observed number of cycle hires and hire time with their respective counterfactuals (what would have been if the pandemic had not happened) to estimate the magnitude of the change caused by the pandemic. The results indicated that following a reduction in cycle hires in March and April 2020, the demand rebounded from May 2020, remaining in the expected range of what would have been if the pandemic had not occurred. This could indicate the resiliency of Santander Cycles. With respect to hire time, an important increase occurred in April, May, and June 2020, indicating that bikes were hired for longer trips, perhaps partly due to a shift from public transit.

A future without drivers? Comparing users', urban planners' and developers' assumptions, hopes, and concerns about autonomous vehicles

AimThis study identifies and compares perceptions of autonomous vehicle (AV) implementation among three Swedish stakeholder groups: Future Users, Urban Planners, and Developers.MethodSemi-structured comparative focus groups were conducted separately with each of the three groups of stakeholders and the transcripts were analysed in broad themes using thematic analysis.ResultsAssumptions, hopes, concerns, and direction of development were the main themes that emerged from the analysis. Assumptions included electrification of vehicles, changes in travel demand, and the need for regulations; Hopes included the idea that AVs will contribute to a more accessible and safer transport system; Concerns included overtrust in AV technology, a possible detrimental impact on the city in the form of congestion and higher demand for investments in infrastructure that could outcompete other modes of transport; and Direction of development and their own role, where the need for collaboration between stakeholders and implementation of AVs in connection with society’s needs were emphasised.ConclusionsAVs were seen to lead to both positive and negative consequences depending on implementation and the development of society. The study shows that dialogue between different stakeholders is lacking but it is desired for the inclusive implementation of AVs.

Modeling and Solving Line Planning with Integrated Mode Choice

We present a mixed-integer linear program (MILP) for line planning with integrated mode and route choice. In contrast to existing approaches, the mode and route decisions are modeled according to the passengers' preferences while commercial solvers can be applied to solve the corresponding MILP. The model aims at finding line plans that maximize the profit for the public transport operator while estimating the corresponding passenger demand with choice models. Both components of profit, revenue and cost, are influenced by the line plan. Hence, the resulting line plans are not only profitable for operators but also attractive to passengers. By suitable preprocessing of the passengers' utilities, we are able to apply any choice model for mode choices using linear constraints. We provide and test means to improve the computational performance. In experiments on the Intercity network of the Randstad, a metropolitan area in the Netherlands, we show the benefits of our model compared to a standard line planning model with fixed passenger demand. Furthermore, we demonstrate with the help of our model the possibilities and limitations for operators when reacting to changes in demand in an optimal way. The results suggest that operators should regularly update their line plan in response to changes in travel demand and estimate the passenger demand during optimization.

Incorporating Passenger Load in Public Transport Systems and its Implementation in Nationwide Models

Abstract Reducing CO2 emissions is crucial to climate change mitigation, but there has been little progress made in the transport sector. Public transport could make a significant contribution, which would require substantial changes in travel demand. In various national transport models proportionally increasing capacities have been assumed; this must be scrutinized in the light of urgency, long-term planning and construction processes and limited financial resources. This paper presents an approach that addresses previous obstacles to the integration of unchanged capacities, namely the lack of a detailed public transport service model and rapidly increasing computation times even in small networks. Based on widely available data on public transport supply and the consideration of previous studies on the relationship between variable vehicle load and the resulting costs for passengers, an extension for a nationwide transport model is developed. In two scenarios – a substantial reduction in local public transport fares and the introduction of a charge for motorized private transport – it is shown that significantly different demand effects occur when passenger load is considered.

Reliable Urban Transportation Network Design Problem Considering Recurrent Traffic Congestions

Traffic congestion is one of the main reasons for the unsustainability of an urban transportation network. Changes in travel demand and streets’ capacity lead to traffic congestion in urban transportation networks, which is known as recurrent traffic congestion. This study aims to assess the performance reliability of urban transportation networks subject to recurrent traffic congestion conditions in order to help travelers to find alternative non-congested routes. A non-congested route is a route without any congested link. The network reliability is defined and modeled as two different scenarios; users’ unawareness of the network’s traffic congestion and users’ ongoing awareness of the network’s traffic congestion. In addition, a reliable network design model is provided to optimize the reliability of the network taking into account street widening policy and budget constraints. Lastly, a Quantum-Inspired Evolutionary Meta-Heuristic Algorithm is adopted; while maintaining accuracy, to reduce problem-solving time and providing the possibility of solving large-scale problems for real networks. To show the applicability of the proposed models and algorithm, they have been implemented on the Sioux Falls transportation network. The results indicate users’ awareness of traffic congestion in the network increases its reliability, and centrally located links are the first candidates for street widening.

Accessibility evaluations of the proposed road user charge (RUC) program in California

With a recently concluded pilot program in the State of California, road user charge (RUC) was explored as an alternative means of transportation funding. The goal was to gather public opinion, address privacy and data security concerns. The pilot program was meant for the future implementation of RUC in the State. However, the impact of RUC on travel demand changes impacting California's industry sectors was not explored or probed. It is expected that by 2020 or later, RUC could have the potential to be implemented in the State causing some initial mode shifts or reduced number of trips made by private vehicles to minimize the cost of travel. This could have a potential impact on employment-based trips to industry sectors of California. Therefore, this paper explores the effects of RUC on the ‘attractiveness’ of four key industry sectors of the State using potential accessibility changes. The changes are evaluated from the RUC implementation for the year 2019 using the year 2013 as the base year. The four key industry sectors used in the evaluation are (i) Manufacturing, (ii) Retail Trade, (iii) Health Care and Social Assistance, and (iv) Accommodation and Food Services. In this evaluation, four vehicle fuel-types are considered, namely gasoline, compressed natural gas (CNG), diesel, and electric for California's rural and urban counties. On average, the percentage change in potential accessibility was found to be largest for counties for the Health Care and Social Assistance industry sector across all fuel types for both rural and urban counties in California. This indicates that the commuters from this industry sector would experience the most considerable benefit with RUC implementation. The lowest average percentage potential accessibility change was observed for the Accommodation and Food Services. This sector's labor force would be the least affected by the RUC implementation for the four industry sectors analyzed.

Health Impact Assessment of Connected and Autonomous Vehicles in San Francisco, Bay Area

This paper presents the potential human health impacts from connected and autonomous vehicles (CAVs) in the San Francisco, Bay Area, U.S. The study concentrates on impacts derived from CAVs’ outcomes on travel demand, safety, and environmental emissions. The paper combines travel modeling practices, critical literature review, and the authors’ expert inference, to quantify the human health impacts using the Integrated Transport and Health Impacts Model (ITHIM). Specifically, ITHIM estimates impact considering changes in travel demand, level of physical activity, emissions, and safety features. This study estimates a 10% increase in car travel mileage and an 11% decrease in walk/bike trip mode share at the presence of CAVs; a 70%–90% crash reduction because of safer CAVs; and significant emission reduction by several CAV-enabled mechanisms such as eco-driving (over 30% CO2 emission reduction) and engine performance adjustment (over 20% CO2 emission reduction). The health impact assessments show significant opportunities for road traffic injury reductions (annual saving of more than 300 deaths and 14,000 life years). However, reduced physical activity because of the mode shift to the car could cause negative health outcomes (increased annual deaths and disability-adjusted life years (DALYs) by more than 30–60 and 700–900, respectively). A set of (active transport [AT] and vehicle electrification) scenarios were developed that could mitigate the potential health-related risks and evaluated their effectiveness. The results show enormous benefits from 50% increased travel-related physical activity which offset the CAV health drawbacks and generates additional benefits by preventing premature deaths and saving life years.

Off-Deployment Traffic Estimation — A Traffic Generative Adversarial Networks Approach

The rapid progress of urbanization has expedited the process of urban planning, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">e.g.</i> , new residential, commercial areas, which in turn boosts the local travel demand. We propose a novel “off-deployment traffic estimation problem”, namely, to foresee the traffic condition changes of a region prior to the deployment of a construction plan. This problem is important to city planners to evaluate and develop urban deployment plans. However, this task is challenging. Traditional traffic estimation approaches lack the ability to solve this problem, since no data about the impact can be collected before the deployment and old data fails to capture the traffic pattern changes. In this paper, we define the off-deployment traffic estimation problem as a traffic generation problem, and develop a novel deep generative model TrafficGAN that captures the shared patterns across spatial regions of how traffic conditions evolve according to travel demand changes and underlying road network structures. In particular, TrafficGAN captures the road network structures through a dynamic filter in the dynamic convolutional layer. We evaluate our TrafficGAN using a large-scale traffic data collected from Shenzhen, China. Results show that TrafficGAN can more accurately estimate the traffic conditions compared with all baselines. We also showcase that TrafficGAN can identify potential traffic issues in some regions and suggest possible reasons.

Effects of Vehicle Restriction Policies on Urban Travel Demand Change from a Built Environment Perspective

License plate restriction (LPR) policy presents the most straightforward way to reduce road traffic and emissions worldwide. However, in practice, it has aroused great controversy. This policy broke the original structure of the urban transportation mode, which needed some matching strategies to adapt to this change. Investigating this travel demand change is a challenging task because it is greatly influenced by features of the local built environment. Fourteen variables from four dimensions, location, land-use diversity, distance to transit, and street design, are used to depict the built environment; moreover, the severe collinearity underlies these feature variables. To solve the multicollinearity among the variables and high-dimensional problem, this study utilizes two different penalization-based regression models, the LASSO (least absolute shrinkage and selection operator) and Elastic Net regression algorithms, to achieve the variable selection and explore the impacts of the built environment on the change of travel demand triggered by the LPR policy. Travel demand changes are assessed by the relative variation in taxi ridership in each traffic analysis zone based on the taxi GPS data. Built environment variables are measured using the transportation network data and the Baidu Map Service points of interest (POI) data. The results show that regions with a higher level of public transportation service and a higher degree of the land mix have a stronger resilience to the vehicle restriction policy. Besides, the contribution rate of public transportation is stable as a whole, while the contribution rate of richness depends on specific types of land use. The conclusions in this study can provide in-depth insights into the influence of the LPR policy and underpin traffic complementary policies to ensure the effectiveness of LPR.