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1.
Children are an often overlooked and understudied population group, whose travel needs are responsible for a significant number
of trips made by a household. In addition, children’s travel and activity participation during the post-school period have
direct implication for adults’ activity-travel patterns. A better understanding of children’s after school activity-travel
patterns and the linkages between parents and children’s activity-travel needs is necessary for accurate prediction and forecasting
of activity-based travel demand modeling systems. In this paper, data from the 2002 Child Development Supplement of the Panel
Study of Income Dynamics is used to undertake a comprehensive assessment of the post-school out-of-home activity-location
engagement patterns of children aged 5–17 years. Specifically, this research effort utilizes a multinomial logit model to
analyze children’s post-school location patterns, and employs a multiple discrete–continuous extreme value model to study
the propensity of children to participate in, and allocate time to, multiple activity episode purpose-location types during
the after-school period. The results show that a wide variety of demographic, attitudinal, environmental, and others’ activity-travel
pattern characteristics impact children’s after school activity engagement patterns. 相似文献
2.
The paper presents a comprehensive validation procedure for the passenger traffic model for Copenhagen based on external data
from the Danish national travel survey and traffic counts. The model was validated for the years 2000–2004, with 2004 being
of particular interest because the Copenhagen Metro became operational in autumn 2002. We observed that forecasts from the
demand sub-models agree well with the data from the 2000 national travel survey, with the mode choice forecasts in particular
being a good match with the observed modal split. The results of the 2000 car assignment model matched the observed traffic
better than those of the transit assignment model. With respect to the metro forecasts, the model over-predicts metro passenger
flows by 10–50%. The wide range of findings from the project resulted in two actions. First, a project was started in January
2005 to upgrade the model’s base trip matrices. Second, a dialog between researchers and the Ministry of Transport has been
initiated to discuss the need to upgrade the Copenhagen model, e.g. a switching to an activity-based paradigm and improving
assignment procedures. 相似文献
3.
In this paper we derive long run structural relationships for all the three classes, viz. upper, second and ordinary second
class, of non-suburban long distance passenger transport demand for Indian railways using annual time series data for 1970–1995.
We employ some of the recent developments in multivariate dynamic econometric time series modeling including estimation of
long-run structural cointegrating relationships, short-run dynamics and measurement of the effects of shocks and their persistence
on evolution of the dynamic passenger transport demand system. The models are estimated using a cointegrating vector autoregressive
(VAR) modeling framework, which allows for endogeneity of regressors. The demand systems are found to be stable for all the
classes in the long run and they converge to equilibrium in a period of around 2–4 years after a typical system-wide shock.
Any disequilibrium in the short-run is corrected in the long-run with adjustments in passenger transport demand and the price
variable, i.e. real rate charged per passenger kilometer. Results show that travel demand in all classes would rise with income,
although the rise is less than proportionate in the case of ordinary class. High price elasticity in long-run and short-run
impulse responses indicate that passenger fare hike could lead to substantial decline in travel demand leading to decline
in revenue earnings of the railways.
This revised version was published online in June 2006 with corrections to the Cover Date. 相似文献
4.
The growth of a city or a metropolis requires well-functioning transit systems to accommodate the ensuing increase in travel demand. As a result, mass transit networks have to develop and expand from simple to complex topological systems over time to meet this demand. Such an evolution in the networks’ structure entails not only a change in network accessibility, but also a change in the level of network reliability on the part of stations and the entire system as well. Network accessibility and reliability are popular measures that have been widely applied to evaluate the resilience and vulnerability of a spatially networked system. However, the use of a single measure, either accessibility or reliability, provides different results, which demand an integrated measure to evaluate the network’s performance comprehensively. In this paper, we propose a set of integrated measures, named ACCREL (Integrated Accessibility and Reliability indicators) that considers both metrics in combination to evaluate a network’s performance and vulnerability. We apply the new measures for hypothetical mass transit system topologies, and a case study of the metro transit system in Seoul follows, highlighting the dynamics of network performance with four evolutionary stages. The main contribution of this study lies in the results from the experiments, which can be used to inform how transport network planning can be prepared to enhance the network functionality, thereby achieving a well-balanced, accessible, and reliable system. Insights on network vulnerability are also drawn for public transportation planners and spatial decision makers. 相似文献
5.
Antti Talvitie 《Transportation》1973,2(2):121-152
This article documents the development of a direct travel demand model for bus and rail modes. In the model, the number of
interzonal work trips is dependent on travel times and travel costs on each available mode, size and socioeconomic characteristics
of the labor force, and the number of jobs.
In estimating the models’ coefficients constraints are imposed to insure that the travel demand elasticities behave according
to the economic theory of consumer behavior.
The direct access time elasticities for both transit modes are estimated to be approximately minus two, and the direct linehaul
time elasticities approximately minus one. The cross-elasticities with respect to the travel time components are estimated
to be less than the corresponding direct elasticities. In general, the time cross-elasticities are such that rail trip characteristics
but not car trip characteristics affect bus travel, and car trip characteristics but not bus trip characteristics affect rail
travel. The cost elasticities lie between zero and one-half.
Thus, the success of mass transit serving a strong downtown appears to depend on good access arrangements. This success can
be confirmed with competitive linehaul speeds. The cost of travel appears to assume a minor role in choice of mode and tripmaking
decisions.
In the paper, a comparison is also made between the predictive performance of the models developed and that of a traditional
transit model. The results indicate that the econometric models developed attain both lower percent error and lower variation
of the error than the traditional model. 相似文献
6.
In this work, laboratory experiment was conducted in order to evaluate the effect of feedback on decision-making under uncertainty,
with and without provided information about travel times. We discuss the prediction of travelers’ response to uncertainty
in two route–choice situations. In the first situation travelers are faced with a route–choice problem in which travel times
are uncertain but some external information about routes’ travel times is provided. The second situation takes place in a
more uncertain environment in which external information about travel times is not provided, and the travelers’ only source
of information is their own experience. Experimental results are in conflict with the paradigm about traveler information
systems: As a consequence of information, the propensity of travelers to minimize expected travel time is not necessarily
increased. Providing travelers with static information about expected travel times reveals an increase in the heterogeneity
of travelers’ choices and reduces the maximization rate. 相似文献
7.
This paper proposes a new activity-based transit assignment model for investigating the scheduling (or timetabling) problem
of transit services in multi-modal transit networks. The proposed model can be used to generate the short-term and long-term
timetables of multimodal transit lines for transit operations and service planning purposes. The interaction between transit
timetables and passenger activity-travel scheduling behaviors is captured by the proposed model, as the activity and travel
choices of transit passengers are considered explicitly in terms of departure time choice, activity/trip chain choices, activity
duration choice, transit line and mode choices. A heuristic solution algorithm which combines the Hooke–Jeeves method and
an iterative supply–demand equilibrium approach is developed to solve the proposed model. Two numerical examples are presented
to illustrate the differences between the activity-based approach and the traditional trip-based method, together with comparison
on the effects of optimal timetables with even and uneven headways. It is shown that the passenger travel scheduling pattern
derived from the activity-based approach is significantly different from that obtained by the trip-based method, and that
a demand-sensitive (with uneven headway) timetable is more efficient than an even-headway timetable. 相似文献
8.
Seyed Mohammad NourbakhshYanfeng Ouyang 《Transportation Research Part B: Methodological》2012,46(1):204-216
Public transit structure is traditionally designed to contain fixed bus routes and predetermined bus stations. This paper presents an alternative flexible-route transit system, in which each bus is allowed to travel across a predetermined area to serve passengers, while these bus service areas collectively form a hybrid “grand” structure that resembles hub-and-spoke and grid networks. We analyze the agency and user cost components of this proposed system in idealized square cities and seek the optimum network layout, service area of each bus, and bus headway, to minimize the total system cost. We compare the performance of the proposed transit system with those of comparable systems (e.g., fixed-route transit network and taxi service), and show how each system is advantageous under certain passenger demand levels. It is found out that under low-to-moderate demand levels, the proposed flexible-route system tends to have the lowest system cost. 相似文献
9.
Optimal transit fare in a bimodal network under demand uncertainty and bounded rationality
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This paper investigates the optimal transit fare in a simple bimodal transportation system that comprises public transport and private car. We consider two new factors: demand uncertainty and bounded rationality. With demand uncertainty, travelers are assumed to consider both the mean travel cost and travel cost variability in their mode choice decision. Under bounded rationality, travelers do not necessarily choose the travel mode of which perceived travel cost is absolutely lower than the one of the other mode. To determine the optimal transit fare, a bi‐level programming is proposed. The upper‐level objective function is to minimize the mean of total travel cost, whereas the lower‐level programming adopts the logit‐based model to describe users' mode choice behaviors. Then a heuristic algorithm based on a sensitivity analysis approach is designed to solve the bi‐level programming. Numerical examples are presented to illustrate the effect of demand uncertainty and bounded rationality on the modal share, optimal transit fare and system performance. Copyright © 2013 John Wiley & Sons, Ltd. 相似文献
10.
Conceptual and empirical models of the propensity to perform social activity–travel behavior are described, which incorporate
the influence of individuals’ social context, namely their social networks. More explicitly, the conceptual model develops
the concepts of egocentric social networks, social activities, and social episodes, and defines the three sets of aspects
that influence the propensity to perform social activities: individuals’ personal attributes, social network composition,
and information and communication technology interaction with social network members. Using the structural equation modeling
(SEM) technique and data recently collected in Toronto, the empirical model tests the effect of these three aspects on the
propensity to perform social activities. Results suggest that the social networks framework provides useful insights into
the role of physical space, social activity types, communication and information technology use, and the importance of “with
whom” the activity was performed with. Overall, explicitly incorporating social networks into the activity–travel behavior
modeling framework provides a promising framework to understand social activities and key aspects of the underlying behavioral
process.
Juan Antonio Carrasco a PhD candidate in Civil Engineering at the University of Toronto, holds a MSc degree in Transportation Engineering from
the Pontificia Universidad Católica de Chile. His doctoral research explores the relationships between social networks, activity–travel
behavior, and ICTs. His research interests also include microsimulation, land use-transportation, and econometric modeling.
Eric J. Miller is Bahen-Tanenbaum Professor of Civil Engineering at the University of Toronto where he is also Director of the Joint Program
in Transportation. His research interests include integrated land-use/transportation modeling, activity-based travel modeling,
microsimulation and sustainable transportation planning. 相似文献
11.
The rapid and continuing changes in travel and mobility needs in India over the last decade necessitates the development and
use of dynamic models for travel demand forecasting rather than cross-sectional models. In this context, this paper investigates
mode choice dynamics among workers in Chennai city, India over a period of five years (1999–2004). Dynamics in mode choice
is captured at four levels: exogenous variable change, state-dependence, changes in users’ sensitivity to attributes, and
unobserved error terms. The results show that the dynamic models provide a substantial improvement (of over 500 log-likelihood
points and ρ2 increases from 44% to 68%) over the cross-sectional model. The performance was compared using two illustrative policy scenarios
with important methodological and practical implications. The results indicate that cross-sectional models tend to provide
inflated estimates of potential improvement measures. Improving the Level of Service (LOS) alone will not produce the anticipated
benefits to transit agencies, as it fails to overcome the persistent inertia captured in the state-dependence factors. The
results and models have important applications in the context of growing motorization and congestion management in developing
countries.
相似文献
P. BhargaviEmail: |
12.
Erika Spissu Abdul Rawoof Pinjari Ram M. Pendyala Chandra R. Bhat 《Transportation》2009,36(4):403-422
In this paper, a joint model of vehicle type choice and utilization is formulated and estimated on a data set of vehicles
drawn from the 2000 San Francisco Bay Area Travel Survey. The joint discrete–continuous model system formulated in this study
explicitly accounts for common unobserved factors that may affect the choice and utilization of a certain vehicle type (i.e.,
self-selection effects). A new copula-based methodology is adopted to facilitate model estimation without imposing restrictive
distribution assumptions on the dependency structures between the errors in the discrete and continuous choice components.
The copula-based methodology is found to provide statistically superior goodness-of-fit when compared with previous estimation
approaches for joint discrete–continuous model systems. The model system, when applied to simulate the impacts of a doubling
in fuel price, shows that individuals are more likely to shift vehicle type choices than vehicle usage patterns.
Erika Spissu is currently a Research Fellow at the University of Cagliari (Italy). She received her Ph.D. from the University of Palermo and University of Cagliari (Italy) in Transport techniques and economics. She spent the past 2 years at The University of Texas at Austin as a Research Scholar focusing primarily in activity-based travel behavior modeling, time use analysis, and travel demand forecasting. Abdul Pinjari is an Assistant Professor in the Department of Civil and Environmental Engineering at the University of South Florida, Tampa. His research interests include time-use and travel-behavior analysis, and activity-based approaches to travel-demand forecasting. He has his Ph.D. from The University of Texas at Austin. Ram M. Pendyala is a Professor of Transportation Systems in the Department of Civil, Environmental, and Sustainable Engineering at Arizona State University. He teaches and conducts research in travel behavior analysis, travel demand modeling and forecasting, activity-based microsimulation approaches, and time use. He specializes in integrated land use-transport models, transport policy formulation, and public transit planning and design. He is currently the Vice-Chair of the International Association for Travel Behavior Research and is the immediate past chair of the Transportation Research Board Committee on Traveler Behavior and Values. He has his PhD from the University of California at Davis. Chandra R. Bhat is a Professor in Transportation at The University of Texas at Austin. He has contributed toward the development of advanced econometric techniques for travel behavior analysis, in recognition of which he received the 2004 Walter L. Huber Award and the 2005 James Laurie Prize from the American Society of Civil Engineers (ASCE), and the 2008 Wilbur S. Smith Distinguished Transportation Educator Award from the Institute of Transportation Engineers (ITE). He is the immediate past chair of the Transportation Research Board Committee on Transportation Demand Forecasting and the International Association for Travel Behaviour Research. 相似文献
Chandra R. Bhat (Corresponding author)Email: |
Erika Spissu is currently a Research Fellow at the University of Cagliari (Italy). She received her Ph.D. from the University of Palermo and University of Cagliari (Italy) in Transport techniques and economics. She spent the past 2 years at The University of Texas at Austin as a Research Scholar focusing primarily in activity-based travel behavior modeling, time use analysis, and travel demand forecasting. Abdul Pinjari is an Assistant Professor in the Department of Civil and Environmental Engineering at the University of South Florida, Tampa. His research interests include time-use and travel-behavior analysis, and activity-based approaches to travel-demand forecasting. He has his Ph.D. from The University of Texas at Austin. Ram M. Pendyala is a Professor of Transportation Systems in the Department of Civil, Environmental, and Sustainable Engineering at Arizona State University. He teaches and conducts research in travel behavior analysis, travel demand modeling and forecasting, activity-based microsimulation approaches, and time use. He specializes in integrated land use-transport models, transport policy formulation, and public transit planning and design. He is currently the Vice-Chair of the International Association for Travel Behavior Research and is the immediate past chair of the Transportation Research Board Committee on Traveler Behavior and Values. He has his PhD from the University of California at Davis. Chandra R. Bhat is a Professor in Transportation at The University of Texas at Austin. He has contributed toward the development of advanced econometric techniques for travel behavior analysis, in recognition of which he received the 2004 Walter L. Huber Award and the 2005 James Laurie Prize from the American Society of Civil Engineers (ASCE), and the 2008 Wilbur S. Smith Distinguished Transportation Educator Award from the Institute of Transportation Engineers (ITE). He is the immediate past chair of the Transportation Research Board Committee on Transportation Demand Forecasting and the International Association for Travel Behaviour Research. 相似文献
13.
《Transportation Research Part C: Emerging Technologies》2000,8(1-6):129-146
This paper presents an integrated transit-oriented travel demand modeling procedure within the framework of geographic information systems (GIS). Focusing on transit network development, this paper presents both the procedure and algorithm for automatically generating both link and line data for transit demand modeling from the conventional street network data using spatial analysis and dynamic segmentation. For this purpose, transit stop digitizing, topology and route system building, and the conversion of route and stop data into link and line data sets are performed. Using spatial analysis, such as the functionality to search arcs nearest from a given node, the nearest stops are identified along the associated links of the transit line, while the topological relation between links and line data sets can also be computed using dynamic segmentation. The advantage of this approach is that street map databases represented by a centerline can be directly used along with the existing legacy urban transportation planning systems (UTPS) type travel modeling packages and existing GIS without incurring the additional cost of purchasing a full-blown transportation GIS package. A small test network is adopted to demonstrate the process and the results. The authors anticipate that the procedure set forth in this paper will be useful to many cities and regional transit agencies in their transit demand modeling process within the integrated GIS-based computing environment. 相似文献
14.
As an innovative combination of conventional fixed-route transit and demand responsive service, flex-route transit is currently the most popular type of flexible transit services. This paper proposes a dynamic station strategy to improve the performance of flex-route transit in operating environments with uncertain travel demand. In this strategy, accepted curb-to-curb stops are labeled as temporary stations, which can be utilized by rejected requests for their pick-up and drop-off. The user cost function is defined as the performance measure of transit systems. Analytical models and simulations are constructed to test the feasibility of implementing the dynamic station strategy in flex-route transit services. The study over a real-life flex-route service indicates that the proposed dynamic station strategy could reduce the user cost by up to 30% without any additional operating cost, when an unexpectedly high travel demand surpasses the designed service capacity of deviation services. 相似文献
15.
Scrutinizing individuals’ leisure-shopping travel decisions to appraise activity-based models of travel demand 总被引:1,自引:0,他引:1
Diana Kusumastuti Els Hannes Davy Janssens Geert Wets Benedict G. C. Dellaert 《Transportation》2010,37(4):647-661
Activity-based models for modeling individuals’ travel demand have come to a new era in addressing individuals’ and households’
travel behavior on a disaggregate level. Quantitative data are mainly used in this domain to enable a realistic representation
of individual choices and a true assessment of the impact of different Travel Demand Management measures. However, qualitative
approaches in data collection are believed to be able to capture aspects of individuals’ travel behavior that cannot be obtained
using quantitative studies, such as detailed decision making process information. Therefore, qualitative methods may deepen
the insight into human’s travel behavior from an agent-based perspective. This paper reports on the application of a qualitative
semi-structured interview method, namely the Causal Network Elicitation Technique (CNET), for eliciting individuals’ thoughts
regarding fun-shopping related travel decisions, i.e. timing, shopping location and transport mode choices. The CNET protocol
encourages participants to think aloud about their considerations when making decisions. These different elicited aspects
are linked with causal relationships and thus, individuals’ mental representations of the task at hand are recorded. This
protocol is tested in the city centre of Hasselt in Belgium, using 26 young adults as respondents. Response data are used
to apply the Association Rules, a fairly common technique in machine learning. Results highlight different interrelated contexts,
instruments and values considered when planning a trip. These findings can give feedback to current AB models to raise their
behavioral realism and to improve modeling accuracy. 相似文献
16.
The aim of this study was to investigate whether a temporary structural change would induce a lasting increase in drivers' public transport use. An experiment targeting 43 drivers was carried out, in which a one-month free bus ticket was given to 23 drivers in an experimental group but not to 20 drivers in a control group. Attitudes toward, habits of, and frequency of using automobile and bus were measured immediately before, immediately after, and one month after the one-month long intervention. The results showed that attitudes toward bus were more positive and that the frequency of bus use increased, whereas the habits of using automobile decreased from before the intervention, even one month after the intervention period. Furthermore, the increase in habitual bus use had the largest effect on the increase in the frequency of bus use. The results suggest that a temporary structural change, such as offering auto drivers a temporary free bus ticket, may be an important travel demand management tool for converting automotive travel demand to public-transport travel demand. 相似文献
17.
Aiming to develop a theoretically consistent framework to estimate travel demand using multiple data sources, this paper first proposes a multi-layered Hierarchical Flow Network (HFN) representation to structurally model different levels of travel demand variables including trip generation, origin/destination matrices, path/link flows, and individual behavior parameters. Different data channels from household travel surveys, smartphone type devices, global position systems, and sensors can be mapped to different layers of the proposed network structure. We introduce Big data-driven Transportation Computational Graph (BTCG), alternatively Beijing Transportation Computational Graph, as the underlying mathematical modeling tool to perform automatic differentiation on layers of composition functions. A feedforward passing on the HFN sequentially implements 3 steps of the traditional 4-step process: trip generation, spatial distribution estimation, and path flow-based traffic assignment, respectively. BTCG can aggregate different layers of partial first-order gradients and use the back-propagation of “loss errors” to update estimated demand variables. A comparative analysis indicates that the proposed methods can effectively integrate different data sources and offer a consistent representation of demand. The proposed methodology is also evaluated under a demonstration network in a Beijing subnetwork. 相似文献
18.
While much of the scholarly literature on immigrants’ travel focuses on transit use, the newest arrivals to the United States
make over twelve times as many trips by carpool as by transit. Using the 2001 National Household Travel Survey and multinomial
logit mode choice models, we examine the determinants of carpooling. In particular, we focus on the likelihood of carpooling
among immigrants—carpooling both within and across households. After controlling for relevant determinants of carpooling,
we find that immigrants are far more likely to form household carpools than native-born adults and also are more likely than
the native-born to form external carpools (outside the household). Moreover, when faced with the options of carpooling and
public transit, immigrants—even recent arrivals—appear to prefer carpools over transit more strongly than the native born. 相似文献
19.
Mobile technologies are generating new business models for urban transport systems, as is evident from recent startups cropping up from the private sector. Public transport systems can make more use of mobile technologies than just for measuring system performance, improving boarding times, or for analyzing travel patterns. A new transaction model is proposed for public transport systems where travelers are allowed to pre-book their fares and trade that demand information to private firms. In this public-private partnership model, fare revenue management is outsourced to third party private firms such as big box retail or large planned events (such as sports stadiums and theme parks), who can issue electronic coupons to travelers to subsidize their fares. This e-coupon pricing model is analyzed using marginal cost theory for the transit service and shown to be quite effective for monopolistic coupon rights, particularly for demand responsive transit systems that feature high cost fares, non-commute travel purposes, and a closed access system with existing pre-booking requirements. However, oligopolistic scenarios analyzed using game theory and network economics suggest that public transport agencies need to take extreme care in planning and implementing such a policy. Otherwise, they risk pushing an equivalent tax on private firms or disrupting the urban economy and real estate values while increasing ridership. 相似文献
20.
The rapid expansion of many Chinese cities has put increasing pressure on existing urban transportation systems. Using Baidu users’ location data, this research analyzes the spatial patterns of the transit systems and commuter flows in Wuhan Metropolitan Area, China, and identifies transit deserts affecting low-income commuters. The results show that, first, most transit demand are generated by trips between neighboring communities, while large transit supply tends to occur between distant communities in the region. Second, about 11.21% of low-income commuters are affected by transit deserts in Wuhan Metropolitan Area. In detail, 61.30% of them commute within the city centers and 36.06% of them commute within the suburbs. Only about 2.64% of them actually travel between city centers and suburbs. Third, for low-income suburban commuters, transit deserts occur when they are surrounded by low-density transit infrastructure and low-frequency transit services, which makes it very difficult for them to connect to rest of the region. However, for low-income commuters residing in the city centers, transit deserts are mainly caused by the large numbers of transit-dependent people competing for limited transit supply in the areas. This research explores the relationship between transit systems and commuting demand in a major Chinese metropolitan area. The findings could help guide future transit system planning in China and beyond. 相似文献