Simulating and analyzing the effect on travel behavior of residential relocation and corresponding traffic demand management strategies

2017 ◽  
Vol 22 (2) ◽  
pp. 837-849 ◽  
Author(s):  
Haoyang Ding ◽  
Min Yang ◽  
Wei Wang ◽  
Chengcheng Xu
Keyword(s):  
Travel Behavior ◽  
Demand Management ◽  
Management Strategies ◽  
Residential Relocation ◽  
Traffic Demand

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 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.

Transport Demand Management by Electronic Fee Collection in a Zone-Based Pricing Scheme: The Stuttgart MobilPASS Field Trial

1997 ◽  
Vol 1576 (1) ◽  
pp. 67-76 ◽  
Author(s):  
Klaus Hug ◽  
Rüdiger Mock-Hecker ◽  
Julian Würtenberger
Keyword(s):  
Public Transportation ◽  
Travel Behavior ◽  
Urban Areas ◽  
Field Trial ◽  
Demand Management ◽  
Field Trials ◽  
Urban Traffic ◽  
Road User ◽  
Traffic Demand ◽  
Mode Of Transport

All attempts to reduce traffic and to change the modal split in favor of public transportation have failed to slow the increase of private transportation in urban areas in recent years. Therefore, urban traffic has become a major problem in many countries. One promising approach to the control of traffic demand in an efficient way is to introduce variable demand-based pricing schemes in urban areas. However, there have been few systematic field trials on the effect of road user charges on travel behavior. The MobilPASS field trial in Stuttgart, Germany, has now investigated the effect of variable road pricing charges on road users’ behavior at a level of detail that is unique. Special attention was paid to the interaction between the charging schemes and reductions in the number of trips, changes of mode of transport, route changes, and time shifts. The empirical results presented indicate that time- and route-dependent road charges have the desired results.

scholarly journals Traffic Demands Policies Effect on Congestion, Delay and Fuel Consumption

2020 ◽  
Vol 9 (5) ◽  
pp. 521-527
Keyword(s):  
Fuel Consumption ◽  
Expert Panel ◽  
Demand Management ◽  
Level Of Service ◽  
System Management ◽  
Transportation Demand ◽  
Traffic Demand ◽  
Study Case ◽  
The Government ◽  
Reduction Percentage

Traffic demands on Jordanian streets have been affected by the increasing human population and the number of vehicles. This study aims to apply transportation demand management (TDM) techniques to improve the level of service (LOS). The study employs both TDM and transportation system management (TSM). In order to investigate what type of strategies to be considered a questionnaire is used. The acceptance degrees of the TDM and TSM groups were measured via the questionnaires using SPSS version 20. The selected policies then are used on a certain location as a study case in Amman city; an intersection is connecting two urban main streets. The used policies have a reduction percentage in traffic demands which is expected throughout an expert panel. The results show that delay and fuel consumption are indeed reduced; however, this does not lead to any considerable improvement in the LOS. The LOS was enhanced when the reduction in traffic demand reached 20% with an increase in capacity achieved by adding 3 new lanes. The fuel consumption and delays were measured to be about 35% less with growth rate of 8% for the coming five years. This study is expected to help popularize TDM policies in place of other solutions so that inexpensive measures can be adopted by the government.

scholarly journals Multi-Label Learning for Appliance Recognition in NILM Using Fryze-Current Decomposition and Convolutional Neural Network

Energies ◽  
2020 ◽  
Vol 13 (16) ◽  
pp. 4154 ◽  
Author(s):  
Anthony Faustine ◽  
Lucas Pereira
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Energy Demand ◽  
Learning Strategy ◽  
Demand Management ◽  
Management Strategies ◽  
Electricity Consumption ◽  
Consumption Pattern ◽  
Similarity Function ◽  
Active Components

The advance in energy-sensing and smart-meter technologies have motivated the use of a Non-Intrusive Load Monitoring (NILM), a data-driven technique that recognizes active end-use appliances by analyzing the data streams coming from these devices. NILM offers an electricity consumption pattern of individual loads at consumer premises, which is crucial in the design of energy efficiency and energy demand management strategies in buildings. Appliance classification, also known as load identification is an essential sub-task for identifying the type and status of an unknown load from appliance features extracted from the aggregate power signal. Most of the existing work for appliance recognition in NILM uses a single-label learning strategy which, assumes only one appliance is active at a time. This assumption ignores the fact that multiple devices can be active simultaneously and requires a perfect event detector to recognize the appliance. In this paper proposes the Convolutional Neural Network (CNN)-based multi-label learning approach, which links multiple loads to an observed aggregate current signal. Our approach applies the Fryze power theory to decompose the current features into active and non-active components and use the Euclidean distance similarity function to transform the decomposed current into an image-like representation which, is used as input to the CNN. Experimental results suggest that the proposed approach is sufficient for recognizing multiple appliances from aggregated measurements.

Exploring the Influence of Consumer Demographics on Online Purchase Benefits

2019 ◽  
Vol 8 (4) ◽  
pp. 303-316
Author(s):  
Suhail Ahmad Bhat ◽  
Mushtaq Ahmad Darzi
Keyword(s):  
Data Collection ◽  
Online Shopping ◽  
Demand Management ◽  
Management Strategies ◽  
Age Groups ◽  
Sampling Technique ◽  
Three Dimensions ◽  
Unique Contribution ◽  
Sociodemographic Variables ◽  
Consumer Demographics

The purpose of the study is to explore the influence of sociodemographic variables (gender & age) on consumers’ perception towards online shopping. The article specifically focuses on purchase benefit that has been categorized into three dimensions such as convenience, interactivity and enjoyment, which are associated with demand management of an Indian consumer. The study has adopted survey by questionnaire method for data collection from online shoppers. Quota sampling technique was used for data collection from 660 e-consumers. Data analysis and hypotheses testing were performed through descriptive (mean, percentage and standard deviation) and comparative statistics (Z-test and one-way ANOVA). Analysis revealed that gender does not have any influence on the purchase benefit variables; however, the significant mean difference was observed between ‘Below 20’ and ‘21–30’ years age groups for convenience and interactivity only. The findings of the current study will be helpful to web store executives in building marketing capabilities and demand management strategies for different age groups. The study has a unique contribution of filling the knowledge gaps in the existing literature by statistically evaluating the role of less-studied sociodemographic variables, that is, age and gender on the consumers’ intention towards purchase benefits associated with online shopping.

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