Urban Travel Demand Research Articles

Measuring long‐run gasoline price elasticities in urban travel demand

AbstractI develop a structural model of urban travel to estimate long‐run gasoline price elasticities. I model the demand for transportation services using a dynamic discrete‐choice model with switching costs and estimate it using a panel dataset with public market‐level data on automobile and public transit use in Chicago. Long‐run own‐ (automobile) and cross‐ (transit) price elasticities are substantially more elastic than short‐run elasticities. Elasticity estimates from static and myopic models are downward biased. I use the estimated model to evaluate the response to several counterfactual policies. A gasoline tax is less regressive after accounting for the long‐run substitution behavior.

Assessing the impacts of COVID-19 on urban passenger travel demand in the greater Toronto area: description of a multi-pronged and multi-staged study with initial results

ABSTRACT The COVID-19 outbreak created a context that was not previously considered by most urban transportation planners and modellers. This paper presents a study design to comprehensively measure the effects of COVID-19 induced Lockdown on changes in travel behaviour and its dynamics. The study is designed for the Greater Toronto Area. The paper presents results from the common portion of four specialized travel surveys. The empirical investigation presented in the paper is on the general pandemic response, in terms of daily activity-travel adaptation behaviour), in the context of around four months of complete Lockdown to stop the spread of COVID-19. Empirical investigation reveals that the Lockdown did not cause any substantial increase in unemployment, but it did force the majority of the residents to telecommute and practice flexible office hours. Overall, results show that Lockdown influences people to engage in more family-oriented activities.

Public Transportation Demand Analysis: A Case Study of Metropolitan Lagos

Modelling, simulation, and forecasting offer a means of facilitating better planning and decision-making. These quantitative approaches can add value beyond traditional methods that do not rely on data and are particularly relevant for public transportation. Lagos is experiencing rapid urbanization and currently has a population of just under 15 million. Both long waiting times and uncertain travel times has driven many people to acquire their own vehicle or use alternative modes of transport. This has significantly increased the number of vehicles on the roads leading to even more traffic and greater traffic congestion. This paper investigates urban travel demand in Lagos and explores passenger dynamics in time and space. Using individual commuter trip data from tickets purchased from the Lagos State Bus Rapid Transit (BRT), the demand patterns through the hours of the day, days of the week and bus stations are analysed. This study aims to quantify demand from actual passenger trips and estimate the impact that dynamic scheduling could have on passenger waiting times. Station segmentation is provided to cluster stations by their demand characteristics in order to tailor specific bus schedules. Intra-day public transportation demand in Lagos BRT is analysed and predictions are compared. Simulations using fixed and dynamic bus scheduling demonstrate that the average waiting time could be reduced by as much as 80%. The load curves, insights and the approach developed will be useful for informing policymaking in Lagos and similar African cities facing the challenges of rapid urbanization.

A bottom-up clustering approach to identify bus driving patterns and to develop bus driving cycles for Hong Kong.

Bus transport has been an important mode taking up a significant share of urban travel demand and thus the corresponding impacts on the environment are of great concerns. Use of driving cycles to evaluate the environmental impacts of buses has attracted much attention in recent years worldwide. The franchised bus service is currently playing important roles in the public transport system in Hong Kong; however, there is no driving cycle developed specifically for them. A set of bus driving cycle was therefore developed using a bottom-up approach where driving data on the bus network with mixed characteristics were collected. Using the Ward's method for clustering, the collected data were then categorized into three clusters representing distinct franchised bus route patterns in Hong Kong. Driving cycles were then developed for each route pattern including (i) congested urban routes with closely spaced bus stops and traffic junctions; (ii) inter-district routes containing a number of stop-and-go activities and a significant portion of smoother high speed driving; and (iii) early morning express routes and mid-night routes connecting remote residential areas and urban areas. These cycles highlighted the unique low-speed and aggressive driving characteristics of bus transport in Hong Kong with frequent stop-and-go activities. The findings from this study would definitely be helpful in assessing the exhaust emissions, fuel consumptions as well as energy consumptions of bus transport. The bottom-up clustering approach adopted in this study would also be useful in identifying specific driving patterns based on vehicle speed trip data with mixed driving characteristics. It is believed that this approach is especially suitable for assessing fixed route public transport modes with mixed driving characteristics.

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.

Urban Spatial Expansion, Urban Compactness, and Average Travel Demand in the US Urbanized Areas

This paper examines the impact of a wide variety of factors on spatial size, spatial compactness measured by population density, and average travel demand measured by daily vehicle miles traveled per capita. The simultaneous equation regression results indicate real average household income has a positive impact on spatial size and average travel demand while a negative impact on urban spatial compactness. Transportation cost measured by fuel cost per mile has a negative impact on spatial size while a positive impact on urban spatial compactness. Among the land use policy tools, urban growth boundary has a positive impact on urban compactness while minimum lot size has a positive impact on urban spatial size.

Spatial-temporal fractal of urban agglomeration travel demand

In modern society, an urban agglomeration is a highly developed spatial form of integrated cities. Travel activities driven by various travel demands frequently take place within an urban agglomeration. Understanding urban agglomeration travel demand is a basic but significant task. The paper constructs spatial–temporal networks for urban agglomeration travel demand via spatial–temporal decomposition and makes explicit analyses of the attributes of the networks. Taiwan and taxi demand, which are a typical urban agglomeration and representative travel demand, respectively, are taken as the empirical objects of the study. It is found that the degree distributions of the spatial–temporal travel demand networks follow power laws, whose exponents monotonically decrease with the growth of the square cells that are used to divide an urban agglomeration to aggregate travel demand. Nevertheless, the fact of following power laws is not influenced by the spatial–temporal granularity of network construction, indicating a spatial–temporal fractal property of urban agglomeration travel demand. The findings contribute to understanding the nature of travel demand and human mobility in the scale of an urban agglomeration.

Comparative Analysis of Travel Patterns from Cellular Network Data and an Urban Travel Demand Model

Data on travel patterns and travel demand are an important input to today’s traffic models used for traffic planning. Traditionally, travel demand is modelled using census data, travel surveys, and traffic counts. Problems arise from the fact that the sample sizes are rather limited and that they are expensive to collect and update the data. Cellular network data are a promising large-scale data source to obtain a better understanding of human mobility. To infer travel demand, we propose a method that starts by extracting trips from cellular network data. To find out which types of trips can be extracted, we use a small-scale cellular network dataset collected from 20 mobile phones together with GPS tracks collected on the same device. Using a large-scale dataset of cellular network data from a Swedish operator for the municipality of Norrköping, we compare the travel demand inferred from cellular network data to the municipality’s existing urban travel demand model as well as public transit tap-ins. The results for the small-scale dataset show that, with the proposed trip extraction methods, the recall (trip detection rate) is about 50% for short trips of 1-2 km, while it is 75–80% for trips of more than 5 km. Similarly, the recall also differs by a travel mode with more than 80% for public transit, 74% for car, but only 53% for bicycle and walking. After aggregating trips into an origin-destination matrix, the correlation is weak (R2<0.2) using the original zoning used in the travel demand model with 189 zones, while it is significant with R2=0.82 when aggregating to 24 zones. We find that the choice of the trip extraction method is crucial for the travel demand estimation as we find systematic differences in the resulting travel demand matrices using two different methods.

Empirical Modeling Analysis of Potential Commute Demand for Carsharing in Shanghai, China

Carsharing is an emerging commute mode in China, which may produce social and environmental benefits. This paper aims to develop a commute mode choice model to explore influential factors and quantify their impacts on the potential demand for carsharing in Shanghai. The sample data were obtained from a revealed preference (RP) and stated preference (SP) survey and integrated with level-of-service attributes from road and transit networks. The RP survey collected commuters’ trip information and socioeconomic and demographic characteristics. In the SP survey, four hypothetical scenarios were designed based on carsharing’s unit price to collect commuters’ willingness to shift to carsharing. Data fusion method was applied to fuse RP and SP models. The joint model identified the target group of choosing carsharing with certain socioeconomic and demographic attributes, such as gender, age, income, household member, household vehicle ownership, and so on. It also indicates that the value of time (VOT) for carsharing is 35.56 RMB Yuan (5.08 US Dollar)/h. The elasticity and marginal effect analysis show that the direct elasticity of carsharing’s fare on its potential demand is −0.660, while the commuters, who have a more urgent plan on car purchase or are more familiar with the carsharing service, have much higher probabilities to choose carsharing as their commute modes. The developed model is expected to be applied to the urban travel demand model, providing references for the formulation of carsharing operation scheme and government policy.

Impact of Urban Sprawl on Travel Demand for Public Transport, Private Transport and Walking

By growing of population, the concept of urban sprawl is increasingly recognized as a serious dilemma among the cities, especially in developing countries. Kabul is one of those cities which are sprawling for two decades and the implication is that residents are suffering from long travel time, pollution and energy consumption. In Kabul city, the number of users for public transport and walk are decreasing day by day and they are shifting to other alternatives. A detailed study to be made in understanding urban sprawl and urban compactness influences on choice mode. This paper is aimed to evaluate the impact of urban sprawl and urban compactness on travel demand for transport modes in 22 districts in Kabul city. Travel demand modeling by using the traditional four-step transportation forecasting model for the year 2017 has been applied to assess the current number of passengers for each transport mode (public transport, walk and private transport) in all 22 districts. Secondly, urban compactness based on metrics with two indicators, mixed-use and density measured to figure out the level of sprawl and compactness in each district. Finally, travel demand for transport modes in both circumstances (compact and sprawl areas) compared in observed districts. The extracted outcome presented an interesting vision of variation amongst districts in Kabul city. This study revealed a profound relationship between urban sprawl and travel demand for all urban transport modes.

Exploring the Weekly Travel Patterns of Private Vehicles Using Automatic Vehicle Identification Data: A Case Study of Wuhan, China

Automatic vehicle identification (AVI) systems collect 24 h vehicle travel data for the efficient management of traffic flows. The automatic vehicle identification data collected by an overhead traffic monitoring system provides a means for understanding urban traffic flows and human mobility. This article explores the weekly travel patterns of private vehicles based on AVI data in Wuhan, a megacity in Central China. We extracted origin–destination information and applied the K-Means clustering algorithm to classify spatial traffic hot spots by camera locations. Subsequently, the Latent Dirichlet Allocation algorithm was used to mine the temporal travel patterns of individual vehicles. The cluster results are summarized in nine travel probability matrixes. The effectiveness of this approach is illustrated by a case study using a large set of AVI data collected from 19 to 24 November 2018, in Wuhan, China. The results revealed six variations of the travel demand on weekdays and weekends—the commuting behaviors of private drivers triggered a tidal change in traffic flows. This study also exposed nine weekly travel patterns for private cars, reflecting temporal similarities of human mobility patterns. We identified four types of commuters. These results can help city managers understand daily changes in urban travel demands.

Understanding Spatio-Temporal Characteristics of Urban Travel Demand Based on the Combination of GWR and GLM

Taxis are an important part of the urban public transit system. Understanding the spatio-temporal variations of taxi travel demand is essential for exploring urban mobility and patterns. The purpose of this study is to use the taxi Global Positioning System (GPS) trajectories collected in New York City to investigate the spatio-temporal characteristic of travel demand and the underlying affecting variables. We analyze the spatial distribution of travel demand in different areas by extracting the locations of pick-ups. The geographically weighted regression (GWR) method is used to capture the spatial heterogeneity in travel demand in different zones, and the generalized linear model (GLM) is applied to further identify key factors affecting travel demand. The results suggest that most taxi trips are concentrated in a fraction of the geographical area. Variables including road density, subway accessibility, Uber vehicle, point of interests (POIs), commercial area, taxi-related accident and commuting time have significant effects on travel demand, but the effects vary from positive to negative across the different zones of the city on weekdays and the weekend. The findings will be helpful to analyze the patterns of urban travel demand, improve efficiency of taxi companies and provide valuable strategies for related polices and managements.

Trip-Chain-Based Travel-Mode-Shares-Driven Framework using Cellular Signaling Data and Web-Based Mapping Service Data

The signaling data of cellular phones, as a passively generated, real-time, wide-coverage, and low-cost data source, have been widely used in recent studies to understand human activity and model urban travel demand. However, in contrast with the Global Positioning System (GPS) data, cellular phone signaling data are sparsely distributed in time and space, which makes travel-mode inference a challenge. Recent studies presented methods of deriving users’ home and work locations, origin-destination trips, and other activities. Very few provided a complete and feasible framework for travel-mode derivation with effective validation methods. This paper provides a real-time travel-mode derivation framework using signaling data and a web-based mapping service. A trip-chain model is proposed to detect individual activity patterns and derive the trips of mobile phone users. Then, the travel mode of each trip is identified by a Fuzzy K-Means model, which is trained and validated by the point-to-point travel time from a web-based mapping service. Finally, the travel-mode shares are aggregated and scaled to the whole population of the study area. The framework is demonstrated using cellular signaling data from 1.9 million users in Shanghai, China for seven days, and citywide point-to-point travel times from a web-based mapping service for three of those seven days. Comparing the modeled travel-mode shares with travel survey data and transportation hub statistics demonstrates the plausibility and efficiency of using a large data source (mobile-trace data and web-based mapping) to accurately assess the travel modes of people in a big city using the proposed framework.

A Copula-Based Joint Model to Capture the Interaction between Mode and Departure Time Choices in Urban Trips

Abstract Mode and departure time choices are two effective decisions on the urban travel demand. Previous studies have different assumptions for modeling these decisions. Some studies assume that mode and departure time choices are independent and other studies consider nested logit as a joint structure. Comparison of joint and independent models shows that these decisions are interrelated. This paper uses a copula-based joint modeling framework to depict this interrelation. To achieve better fitted model several copula functions including product, AMH and Frank are used. For mode choice and departure time choice models, multinomial logit and bivariate logit models are used, respectively and finally, a BL-MNL joint model is used to create linkage between mode and departure time choices. The data used in this study is drawn from the origin-destination data of Qazvin-Iran in 2010. Estimated copula dependence parameters with high statistical significance, approve the interrelation between error terms of two models.

Estimation of inter-city travel demand for public road transport in Nigeria

This paper models an intercity travel demand which uses the principal structural variables that have been identified in the literature. This research develops an urban public travel demand model for 19 directional O-D city-pair network originating from Owerri Urban. Using revealed preference data from the period of 2014 to 2016 which was filtered into 207 observations operated by the 16 transport companies in Owerri, Imo State. The empirical analysis explicitly modelled the pattern of correlations among service variables by a log-linear model using OLS. The estimates yield better demand elasticities than those of direct linear models. Other empirical findings include that; overall fares elasticities are low, so that increases in fare levels will almost always lead to increases in revenue. However, it is possible for any would-be operator determine the expected patronage on any route of operation using the derived models from this paper. Thus this paper has come up with empirical model to assess the viability of intercity passenger transport operation in Nigeria. However, it will also help the operators in business to do a sensitivity analysis based of changes in the intercity passenger travel markets in Nigeria.

Measuring Long-Run Price Elasticities in Urban Travel Demand

I develop a structural model of urban travel to estimate long run gasoline price elasticities. I model the demand for transportation services using a dynamic discrete-choice model with switching costs and estimate it using a panel dataset with public market-level data on automobile and public transit use in Chicago. Long-run own- (automobile) and cross- (transit) price elasticities are substantially more elastic than short-run elasticities. Elasticity estimates from static and myopic models are downward biased. I use the estimated model to evaluate the response to several counterfactual policies. A gasoline tax is less regressive after accounting for the long-run substitution behavior.

Analysis of Urban Residents' Travel Demand Based on Multiple Data

In the areas where public transport system is undeveloped, IC card data can’t fully represent the residents' demand, and affect the analysis of urban residents' demand. The clustering analysis theory is used to analyze the travel original and destination after eliminate the invalid and redundant data. Reappear the original and destination on the GIS map, integrate the data and draw desire lines. This paper discusses the comprehensiveness of bus travel demand analysis based on bus and Didi data.

Estimating the activity types of transit travelers using smart card transaction data: a case study of Singapore

Understanding individual daily activity patterns is essential for travel demand management and urban planning. This research introduces a new method to infer transit riders’ activities from their smart card transaction records. Using Singapore as an example, activity type classification models were built using household travel survey and a rich set of urban built environment measures to reveal the spatial and temporal correspondences that indicate the activity participation of transit riders. The calibrated model is then applied to the transit smart card dataset to extract the embedded activity information. The proposed approach enables to spatially and temporally quantify, visualize, and examine urban activity landscapes in a metropolitan area and provides real-time decision support for the city. This study also demonstrates the potential value of combining new ‘‘big data’’ such as transit smart card data and “small data” such as traditional travel surveys to create better insights of urban travel demand and activity dynamics.

A Trajectory Regression Clustering Technique Combining a Novel Fuzzy C-Means Clustering Algorithm with the Least Squares Method

Rapidly growing GPS (Global Positioning System) trajectories hide much valuable information, such as city road planning, urban travel demand, and population migration. In order to mine the hidden information and to capture better clustering results, a trajectory regression clustering method (an unsupervised trajectory clustering method) is proposed to reduce local information loss of the trajectory and to avoid getting stuck in the local optimum. Using this method, we first define our new concept of trajectory clustering and construct a novel partitioning (angle-based partitioning) method of line segments; second, the Lagrange-based method and Hausdorff-based K-means++ are integrated in fuzzy C-means (FCM) clustering, which are used to maintain the stability and the robustness of the clustering process; finally, least squares regression model is employed to achieve regression clustering of the trajectory. In our experiment, the performance and effectiveness of our method is validated against real-world taxi GPS data. When comparing our clustering algorithm with the partition-based clustering algorithms (K-means, K-median, and FCM), our experimental results demonstrate that the presented method is more effective and generates a more reasonable trajectory.

Financing Indian Urban Rail through Land Development: Case Studies and Implications for the Accelerated Reduction in Oil Associated with 1.5 °C

Urban travel demand and oil dependence need dramatic change to achieve the 1.5 °C degree target especially with the electrification of all land-based passenger transport and the decarbonizing of electric power. In this article we investigate the transition of ‘oil-based automobile dependence’ to ‘urban rail plus renewable energy’ to cater for transport demand in Indian cities. India is perceived to be a key driver of global oil demand in coming decades due to the potential increase in car use driven by a fast growing national average income. However, it is possible that India could surprise the world by aggressively pursuing an electrified transit agenda within and between cities and associated supporting local transport with electric vehicles, together with renewable power to fuel this transport. The changes will require two innovations that this article focuses on. First, innovative financing of urban and intercity rail through land-based finances as funding and financing of such projects has been a global challenge. Second, enabling Indian cities to rapidly adopt solar energy for all its electrified transport systems over oil plus car dependence. The article suggests that Indian cities may contribute substantially to the 1.5 °C agenda as both policies appear to be working.