scholarly journals Spatiotemporal Polyrhythm Characteristics of Public Bicycle Mobility in Urban Chronotopes Context

2021 ◽  
Vol 11 (1) ◽  
pp. 6
Author(s):  
Lijun Chen ◽  
Shangjing Jiang
Keyword(s):  
Travel Behavior ◽  
Travel Demand ◽  
Demand Management ◽  
Active Travel ◽  
Full Sample ◽  
Inflection Points ◽  
Sample Data ◽  
Behavioral Preference ◽  
Source Sink ◽  
Cycling Speed

Cycling rhythm performance is the result of a complex interplay between active travel demand and cycling network supply. Most studies focused on bicycle flow, but little attention has been paid to cycling rhythm changes for public bicycles. Full sample data of origin–destination enables an efficient description of network-wide cycling mobility efficiency in urban public bicycle systems. In this paper, we show how the spatiotemporal characteristics of cycling speed reveal the performance of cycling rhythms. The inference method of riding speed estimation is proposed with an unknown cycling path. The significant inconsistency of docking stations in cycling rhythm was unraveled by the source–sink relationship comparison. The asymmetry of the cycling rhythm on the path is manifested as the rhythm difference among paths and bidirectional inconsistency. We found that cycling rhythm has a temporal multilayer and spatial mismatch, which shows the inflection points of the cycling rhythm where the travel behavioral preference changes and the exact road segments with different rhythms. This finding suggests that a well-designed cycling environment and occupation-residential function should be considered in active transport demand management and urban planning to help induce active travel behavior decisions.

Linking Residential Parking to Automobile Transportation Impact Outcomes at a Development Level

2020 ◽  
pp. 036119812096479
Author(s):  
Kristina M. Currans ◽  
Gabriella Abou-Zeid ◽  
Nicole Iroz-Elardo
Keyword(s):  
Travel Behavior ◽  
Travel Demand ◽  
Demand Management ◽  
Management Strategies ◽  
Land Development ◽  
Future Research ◽  
Alternative Mode ◽  
Travel Demand Management ◽  
Development Level ◽  
Automobile Demand

Although there exists a well-studied relationship between parking policies and automobile demand, conventional practices evaluating the transportation impacts of new land development tend to ignore this. In this paper, we: (a) explore literature linking parking policies and vehicle use (including vehicle trip generation, vehicle miles traveled [VMT], and trip length) through the lens of development-level evaluations (e.g., transportation impact analyses [TIA]); (b) develop a conceptual map linking development-level parking characteristics and vehicle use outcomes based on previously supported theory and frameworks; and (c) evaluate and discuss the conventional approach to identify the steps needed to operationalize this link, specifically for residential development. Our findings indicate a significant and noteworthy dearth of studies incorporating parking constraints into travel behavior studies—including, but not limited to: parking supply, costs or pricing, and travel demand management strategies such as the impacts of (un)bundled parking in housing costs. Disregarding parking in TIAs ignores a significant indicator in automobile use. Further, unconstrained parking may encourage increases in car ownership, vehicle trips, and VMT in areas with robust alternative-mode networks and accessibility, thus creating greater demand for vehicle travel than would otherwise occur. The conceptual map offers a means for operationalizing the links between: the built environment; socio-economic and demographic characteristics; fixed and variable travel costs; and vehicle use. Implications for practice and future research are explored.

scholarly journals SOLVING TRAFFIC CONGESTION FROM THE DEMAND SIDE

2015 ◽  
Vol 27 (6) ◽  
pp. 529-538 ◽  
Author(s):  
Ying-En Ge ◽  
Olegas Prentkovskis ◽  
Chunyan Tang ◽  
Wafaa Saleh ◽  
Michael G. H. Bell ◽  
...  
Keyword(s):  
Travel Behavior ◽  
Traffic Congestion ◽  
Travel Demand ◽  
State Of The Art ◽  
Demand Management ◽  
Relevant Literature ◽  
Short Review ◽  
Active Management ◽  
Demand Side ◽  
Travel Demand Management

It is nowadays widely accepted that solving traffic congestion from the demand side is more important and more feasible than offering more capacity or facilities for transportation. Following a brief overview of evolution of the concept of Travel Demand Management (TDM), there is a discussion on the TDM foundations that include demand-side strategies, traveler choice and application settings and the new dimensions that ATDM (Active forms of Transportation and Demand Management) bring to TDM, i.e. active management and integrative management. Subsequently, the authors provide a short review of the state-of-the-art TDM focusing on relevant literature published since 2000. Next, we highlight five TDM topics that are currently hot: traffic congestion pricing, public transit and bicycles, travel behavior, travel plans and methodology. The paper closes with some concluding remarks.

scholarly journals Multimode Traffic Travel Behavior Characteristics Analysis and Congestion Governance Research

2020 ◽  
Vol 2020 ◽  
pp. 1-8
Author(s):  
Wen Li ◽  
Wei Feng ◽  
Hua-zhi Yuan
Keyword(s):  
Sustainable Development ◽  
Public Transportation ◽  
Travel Behavior ◽  
Traffic Congestion ◽  
Travel Demand ◽  
Demand Management ◽  
Congestion Management ◽  
Traffic Demand ◽  
Modern Cities ◽  
Behavior Characteristics

The rapid aggregation of modern urban population and the rapid growth of car travel lead to traffic congestion, environmental pollution, and other problems. In view of the limited land resources in our country, it is impractical to meet residents’ travel demand by blindly increasing traffic supply. Therefore, addressing the urban road congestion problem for sustainable development of modern cities, the paper makes research on residents’ travel behavior characteristics and travel preference under the condition of multimodal transportation to formulate reasonable traffic demand management strategy for the guide on public traffic demand, bus priority strategy, and congestion management. The operation characteristic of each transportation mode is analyzed by comparing its related traffic and economic characteristics. Multimode traffic choice behavior is discussed by establishing multiple logistic regression models to analyze the main influencing factors to travelers’ social and economic attributes, travel characteristics, and preference based on travel survey data of urban residents. The paper proposes the development of an urban public transportation system and travelling mode shift from cars to public transportation as reasonable travel structure for congestion management and sustainable development of modern cities.

Household Activity Rescheduling in Response to Automobile Reduction Scenarios

2002 ◽  
Vol 1807 (1) ◽  
pp. 174-181 ◽  
Author(s):  
Sean T. Doherty ◽  
Martin Lee-Gosselin ◽  
Kyle Burns ◽  
Jean Andrey
Keyword(s):  
Travel Behavior ◽  
Travel Demand ◽  
Demand Management ◽  
Small Sample ◽  
Lifestyle Changes ◽  
Travel Demand Management ◽  
Fuel Prices ◽  
Response Strategies ◽  
Household Members

Forecasting the enduring and wider implications of emerging travel demand management and automobile reduction policies has proved to be a challenging task. Travel behavior researchers point to the need for more in-depth research into the underlying activity-travel scheduling processes as a means to improve the ability to do so. The objective of this research is to explore the household rescheduling and adaptation process to vehicle reduction scenarios. Descriptive results from two, small-sample, in-depth experiments are presented. The first experiment focused on households’ response to a fuel prices increase, whereas the second focused on the response of two-vehicle households to long-term removal of one vehicle from the household. Results indicate that households are aware of a broad range of possible adaptation strategies, including not only mode changes but also a wide variety of changes in activities, planning, and longer-term lifestyle changes. When people were asked to actually implement such stated strategies under realistic conditions, a much more elaborate behavioral response was elicited. This included multiple rescheduling decisions involving several activities and household members over the course of a day or even several days. Thus, even relatively straightforward stated response strategies often lead to interconnected primary and secondary effects on observed activities and travel, realized through a sequence of rescheduling decisions over time and space and across household members. These results suggest that an explicit accounting of rescheduling decision sequences in forecasting models would enhance their behavioral validity and accuracy.

scholarly journals MODELING A SPATIO-TEMPORAL INDIVIDUAL TRAVEL BEHAVIOR USING GEOTAGGED SOCIAL NETWORK DATA: A CASE STUDY OF GREATER CINCINNATI

2017 ◽  
Vol IV-4/W2 ◽  
pp. 207-214
Author(s):  
M. Saeedimoghaddam ◽  
C. Kim
Keyword(s):  
Travel Behavior ◽  
Travel Demand ◽  
Predictive Accuracy ◽  
Demand Management ◽  
Large Data ◽  
Data Sources ◽  
Mobile Phone Data ◽  
Travel Demand Management ◽  
Travel Destination ◽  
Spatio Temporal

Understanding individual travel behavior is vital in travel demand management as well as in urban and transportation planning. New data sources including mobile phone data and location-based social media (LBSM) data allow us to understand mobility behavior on an unprecedented level of details. Recent studies of trip purpose prediction tend to use machine learning (ML) methods, since they generally produce high levels of predictive accuracy. Few studies used LSBM as a large data source to extend its potential in predicting individual travel destination using ML techniques. In the presented research, we created a spatio-temporal probabilistic model based on an ensemble ML framework named “Random Forests” utilizing the travel extracted from geotagged Tweets in 419 census tracts of Greater Cincinnati area for predicting the tract ID of an individual’s travel destination at any time using the information of its origin. We evaluated the model accuracy using the travels extracted from the Tweets themselves as well as the travels from household travel survey. The Tweets and survey based travels that start from same tract in the south western parts of the study area is more likely to select same destination compare to the other parts. Also, both Tweets and survey based travels were affected by the attraction points in the downtown of Cincinnati and the tracts in the north eastern part of the area. Finally, both evaluations show that the model predictions are acceptable, but it cannot predict destination using inputs from other data sources as precise as the Tweets based data.

scholarly journals Do Multi-Use-Path Accessibility and Clustering Effect Play a Role in Residents' Choice of Walking and Cycling?

2021 ◽  
Author(s):  
Chih-Hao Wang ◽  
Na Chen
Keyword(s):  
Built Environment ◽  
Spatial Autocorrelation ◽  
Travel Behavior ◽  
Travel Demand ◽  
Salt Lake ◽  
Probit Model ◽  
Active Travel ◽  
Spatial Autoregressive ◽  
Clustering Effect ◽  
Level 1

The transportation studies literature recognizes the relationship between accessibility and active travel. However, there is limited research on the specific impact of walking and cycling accessibility to multi-use paths on active travel behavior. Combined with the culture of automobile dependency in the US, this knowledge gap has been making it difficult for policy-makers to encourage walking and cycling mode choices, highlighting the need to promote a walking and cycling culture in cities. In this case, a clustering effect (“you bike, I bike”) can be used as leverage to initiate such a trend. This project contributes to the literature as one of the few published research projects that considers all typical categories of explanatory variables (individual and household socioeconomics, local built environment features, and travel and residential choice attitudes) as well as two new variables (accessibility to multi-use paths calculated by ArcGIS and a clustering effect represented by spatial autocorrelation) at two levels (level 1: binary choice of cycling/waking; level 2: cycling/walking time if yes at level 1) to better understand active travel demand. We use data from the 2012 Utah Travel Survey. At the first level, we use a spatial probit model to identify whether and why Salt Lake City residents walked or cycled. The second level is the development of a spatial autoregressive model for walkers and cyclists to examine what factors affect their travel time when using walking or cycling modes. The results from both levels, obtained while controlling for individual, attitudinal, and built-environment variables, show that accessibility to multi-use paths and a clustering effect (spatial autocorrelation) influence active travel behavior in different ways. Specifically, a cyclist is likely to cycle more when seeing more cyclists around. These findings provide analytical evidence to decision-makers for efficiently evaluating and deciding between plans and policies to enhance active transportation based on the two modeling approaches to assessing travel behavior described above.

Using Deep Learning to Understand Travel Demands in Different Urban Districts

2021 ◽  
pp. 036119812199458
Author(s):  
Shunhua Bai ◽  
Junfeng Jiao
Keyword(s):  
Built Environment ◽  
Travel Behavior ◽  
Prediction Accuracy ◽  
Travel Demand ◽  
Short Term Memory ◽  
Future Research ◽  
Demand Forecast ◽  
Time And Space ◽  
The University ◽  
The Impact

Travel demand forecast plays an important role in transportation planning. Classic models often predict people’s travel behavior based on the physical built environment in a linear fashion. Many scholars have tried to understand built environments’ predictive power on people’s travel behavior using big-data methods. However, few empirical studies have discussed how the impact might vary across time and space. To fill this research gap, this study used 2019 anonymous smartphone GPS data and built a long short-term memory (LSTM) recurrent neural network (RNN) to predict the daily travel demand to six destinations in Austin, Texas: downtown, the university, the airport, an inner-ring point-of-interest (POI) cluster, a suburban POI cluster, and an urban-fringe POI cluster. By comparing the prediction results, we found that: the model underestimated the traffic surge for the university in the fall semester and overestimated the demand for downtown on non-working days; the prediction accuracy for POI clusters was negatively related to their adjacency to downtown; and different POI clusters had cases of under- or overestimation on different occasions. This study reveals that the impact of destination attributes on people’s travel demand can vary across time and space because of their heterogeneous nature. Future research on travel behavior and built environment modeling should incorporate the temporal inconsistency to achieve better prediction accuracy.

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