Aggregate Travel Demand Research Articles

Unraveling urban dynamics: Scaling laws of human mobility across the cityscape divide

The scaling laws of human mobility are one of the key theories of human geography. They also have important policy implications for urban-rural integration and regional economic growth. Although scaling laws of human mobility have been widely discussed, the allometric growth between urban and rural areas is overlooked. This study aims to fill the gap by investigating scaling laws and power-law distribution across urban and rural areas during urban growth with individual people's trajectory information of 13 million mobile phone users. The analysis identified a turning point at a population of 1.8 million in the Greater Bay Area, where the growth rate of aggregate travel demand stabilizes. The results show that human mobility exhibits segmented allometric growth, with superlinear growth transitioning to sublinear beyond this turning point. The disparities of travel behaviour patterns between urban and rural populations increase as cities grow. These findings would enhance the existing knowledge of the allometric scaling of human mobility.

Regional Bicycle Network Evaluation and Strategic Planning: A Quantitative Methodological Approach Despite Limited Data Sources for Cycling

In cases where budgets and space are limited, the realization of new bicycle infrastructure is often hard, as an evaluation of the existing network or the benefits of new investments is rarely possible. Travel demand models can offer a tool to support decision makers, but because of limited data availability for cycling, the validity of the demand estimation and trip assignment are often questionable. This paper presents a quantitative method to evaluate a bicycle network and plan strategic improvements, despite limited data sources for cycling. The proposed method is based on a multimodal aggregate travel demand model. Instead of evaluating the effects of network improvements on the modal split as well as link and flow volumes, this method works the other way around. A desired modal share for cycling is set, and the resulting link and flow volumes are the basis for a hypothetical bicycle network that is able to satisfy this demand. The current bicycle network is compared with the hypothetical network, resulting in preferable actions and a ranking based on the importance and potentials to improve the modal share for cycling. Necessary accompanying measures for other transport modes can also be derived using this method. For example, our test case, a city in Austria with 300,000 inhabitants, showed that a shift of short trips in the inner city toward cycling would, without countermeasures, provide capacity for new longer car trips. The proposed method can be applied to existing travel models that already contain a mode choice model.

Ridesharing services and urban transport CO2 emissions: Simulation-based evidence from 247 cities

• We simulate the evolution of urban transport CO 2 emissions in 247 cities up to 2050. • We explore a counterfactual scenario, in which ridesharing proliferates much faster. • The widespread proliferation of ridesharing could reduce the aggregate CO 2 by 6%. • We show why the effect widely differs across cities and regions of the world. • We discuss the role of ridesharing and related policies in greening future cities. Could a widespread proliferation of ridesharing services mitigate or exacerbate the carbon footprint of urban passenger transport? Despite having profound policy implications, this question has not yet been answered in the literature. This paper examines that impact ex-ante , by simulating the aggregate travel demand, the choice of transport mode and the resulting CO 2 emissions in 247 cities between 2015 and 2050. We find that if ridesharing services receive substantial policy support, CO 2 emissions from passenger transport in 2050 will be on average 6.3% lower than their reference level. However, we show that this finding differs widely across cities. The paper identifies the reasons for this variation and the policies that are socially desirable in a given city, conditional on its characteristics.

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.

How can public transit get people out of their cars? An analysis of transit mode choice for commute trips in Los Angeles

U.S. public transit agencies struggle to attract and retain riders. Unprecedented public investments have been made over the past several decades for expanding and improving transit service across cities. Unfortunately, however, there is no evidence of increase in ridership once growth in population and aggregate travel demand are accounted for. Consequently, the quest for boosting patronage continues. The challenge, experts argue, is to attract people out of cars.In this paper, I use a recent state-wide travel survey from California and take advantage of a new comprehensive historical archive of regional real-time multi-modal transportation system data to explore contexts in which persons belonging to car-owning households within Los Angeles County use transit for their commute.I find that few car owners use transit, and that lack of access to the household vehicle(s) explain choice of transit to a large extent. While discretionary transit use (or transit use by choice) is rare, I find evidence that fast (relative to car), frequent and reliable transit service along with fewer transfer requirements strongly correlate with car-owners’ transit mode choice. Home and workplace neighborhood density, proximity to transit stop, and availability of rail are other critical facilitators. Even if observed effects are due, in part, to self-selection, there are important lessons for transit planners. For example, results suggest that all else equal: reduction in transit-to-auto travel time ratio by unity can increase odds of transit mode choice by about 25%; reduction in headway by 10min can increase the odds by about 30%, and; lowering the standard deviation of schedule deviation from over to under three minutes can result in 2.6 times increase in the odds.This paper identifies effective strategies for increasing transit's competitiveness relative to auto, and hence attracting people out of their cars. While rail network expansion programs and transit-oriented development efforts must continue across U.S. cities, it is important that planners also advocate for investments in key dimensions of bus service quality that patrons value, such as speed, frequency and reliability. Efficient network designs that reduce transfer requirements, introduction of bus rapid transit services, and improvements in real-time operations, scheduling and long-range planning by using ITS (intelligent transportation systems) infrastructures across modes are critical. This study shows that careful planning can promote discretionary transit use by attracting existing latent demand and by creating new demand in an era of increasing government interest in transit and growing traffic congestion. Broader positive effects on the travelling public and the environment are much greater than what this study can predict.

Travel trends among young adults in Germany: increasing multimodality and declining car use for men

After decades of growth in motorization and car use, recent trends suggest stagnating travel demand in Germany. This paper focuses on travel trends of young German adults between 18 and 29. For decades these young adults represented one of the most car oriented age groups. Until the 1990s car use increased for all age groups in Germany, including young adults. Based on a range of primary and secondary data sources this paper finds that since the turn of the millennium car use among young adults has decreased. We identify two important underlying trends. First, an increasing share of young drivers also uses alternative modes of transport, thus indicating a rise in multimodal travel behavior. Second, gender differences in car travel have largely disappeared among young Germans—mainly because young men reduced car ownership and driving more than young women. These trends have led to an overall decrease of automobile travel by young adults and contributed to an increase of travel by other modes of transport. Decreasing automobile travel by young adults helps explain the stagnation of aggregate travel demand in Germany, since declining car use among young adults offsets increases in automobile travel of older individuals.

ASSESSMENT OF INTEGRATED ENVIRONMENTAL STRATEGIES FOR METRO MANILA

Studies and government programs are reviewed to develop environmental strategies. BAU scenarios for 2005, 2010 and 2015, implementation of motor vehicle inspection system (MVIS), transportation demand management, replacement of 2-stroke tricycles with 4-stroke tricycles, construction of bike lanes, expansion of railway network, installation of diesel particulate traps (DPTs) for public transport vehicles, use of alternative fuels in public transport and policy combinations are assessed in terms of total PM emissions based on the emission model and aggregate travel demand forecasting models and database of the 1996 Metro Manila Urban Transportation Integration Study. Measures such as the MVIS and introduction of 4-stroke tricycles are the single policies that effected higher reduction in emissions from 11-16 tons/day in 2010 to around 12-16 tons/day in 2015. These are followed by the railway network expansion with reduction of 11 tons/day in 2015 and by the installation of DPTs in public transport vehicles.

Long-Term Aggregate Travel Demand Analysis: A Comparison between Japan and Taiwan

Long-Term Aggregate Travel Demand Analysis: A Comparison between Japan and Taiwan

A comprehensive analysis of built environment characteristics on household residential choice and auto ownership levels

There has been an increasing interest in the land use-transportation connection in the past decade, motivated by the possibility that design policies associated with the built environment can be used to control, manage, and shape individual traveler behavior and aggregate travel demand. In this line of research and application pursuit, it is critical to understand whether the empirically observed association between the built environment and travel behavior-related variables is a true reflection of underlying causality or simply a spurious correlation attributable to the intervening relationship between the built environment and the characteristics of people who choose to live in particular built environments. In this research paper, we identify the research designs and methodologies that may be used to test the presence of “true” causality versus residential sorting-based “spurious” associations in the land-use transportation connection. The paper then develops a methodological formulation to control for residential sorting effects in the analysis of the effect of built environment attributes on travel behavior-related choices. The formulation is applied to comprehensively examine the impact of the built environment, transportation network attributes, and demographic characteristics on residential choice and car ownership decisions. The model formulation takes the form of a joint mixed multinomial logit-ordered response structure that (a) accommodates differential sensitivity to the built environment and transportation network variables due to both demographic and unobserved household attributes and (b) controls for the self-selection of individuals into neighborhoods based on car ownership preferences stemming from both demographic characteristics and unobserved household factors. The analysis in the paper represents, to our knowledge, the first instance of the formulation and application of a unified mixed multinomial logit-ordered response structure in the econometric literature. The empirical analysis in the paper is based on the residential choice and car ownership decisions of San Francisco Bay area residents.

Calibrating aggregate travel demand models with traffic counts: Estimators and statistical performance

Traffic counts on network links constitute an information source on travel demand which is easy to collect, cheap and repeatable. Many models proposed in recent years deal with the use of traffic counts to estimate Origin/Destination (O/D) trip matrices under different assumptions on the type of "a-priori" information available on the demand (surveys, outdated estimates, models, etc.) and the type of network and assignment mapping (see Cascetta & Nguyen 1988). Less attention has been paid to the possibility of using traffic counts to estimate the parameters of demand models. In this case most of the proposed methods are relative to particular demand model structures (e.g. gravity-type) and the statistical analysis of estimator performance is not thoroughly carried out. In this paper a general statistical framework defining Maximum Likelihood, Non Linear Generalized Least Squares (NGLS) and Bayes estimators of aggregated demand model parameters combining counts-based information with other sources (sample or a priori estimates) is proposed first, thus extending and generalizing previous work by the authors (Cascetta & Russo 1992). Subsequently a solution algorithm of the projected-gradient type is proposed for the NGLS estimator given its convenient theoretical and computational properties. The algorithm is based on a combination of analytical/numerical derivates in order to make the estimator applicable to general demand models. Statistical performances of the proposed estimators are evaluated on a small test network through a Monte Carlo method by repeatedly sampling "starting estimates" of the (known) parameters of a generation/distribution/modal split/assignment system of models. Tests were carried out assuming different levels of "quality" of starting estimates and numbers of available counts. Finally NGLS estimator was applied to the calibration of the described model system on the network of a real medium-size Italian town using real counts with very satisfactory results in terms of both parameter values and counted flows reproduction.

A cluster analysis of disaggregate decision‐making processes in travel mode‐choice behavior

Understanding the process of decision?making by which an individual chooses a travel mode is important for transportation researchers in constructing disaggregate travel behavior models and estimating aggregate travel demand. In order to determine if a common decision process applies to the mode?choice behavior of the majority travellers, the data of a transportation attitudinal survey was examined in a cluster analysis. Four basic decision models were tested: the linear additive model, the conjunctive?maximin model, the lexicographic model, and the lexicographic?conjunctive model. The cluster analysis identified three homogeneous groups, based on their distinctive patterns of decision?making. This suggests that different processes of decision?making exist in travel mode?choice behavior. Comparisons between different clustering criteria indicate that decision rules are more effective than travel attributes in differentiating travellers? mode?choice behavior.

The Trip as a Good in Aggregate Travel Demand Modeling

The Trip as a Good in Aggregate Travel Demand Modeling