In this study, we examine the impacts that EVs （electric vehicles） have on vehicle usage patterns and environmental improvements, using our integrated travel demand forecasting model, which can simulate an individua...In this study, we examine the impacts that EVs （electric vehicles） have on vehicle usage patterns and environmental improvements, using our integrated travel demand forecasting model, which can simulate an individual activity-travel behavior in each time period, as well as consider an induced demand by decreasing travel cost. In order to examine the effects that charging/discharging have on the demand in electricity, we analyze scenarios based on the simulation results of the EVs＇ parking location, parking duration and the battery state of charge. From the simulation, result under the ownership rate of EVs in the Nagoya metropolitan area in 2020 is about 6%, which turns out that the total CO2 emissions have decreased by 4% although the situation of urban transport is not changed. After calculating the electricity demand in each zone using architectural area and basic units of hourly power consumption, we evaluate the effect to decrease the peak load by V2G （vehicle-to-grid）. According to the results, if EV drivers charge at home during the night and discharge at work during the day, the electricity demand in Nagoya city increases by approximately 1%, although changes in each individual zone range from -7% to ＋8%, depending on its characteristics.展开更多
Promoting sustainable mobility and understanding travel demand are critical for rapidly growing cities like Kigali.This research aims to address limitations of traditional transport models by integrating geospatial an...Promoting sustainable mobility and understanding travel demand are critical for rapidly growing cities like Kigali.This research aims to address limitations of traditional transport models by integrating geospatial analysis to support multimodal planning and optimize bike-sharing infrastructure.The study combines the Four-Step Transport Model with Geographic Information Systems(GIS)to enhance spatial disaggregation and identify optimal bike-sharing station locations.It incorporates shortest-path analysis and accounts for topography,road networks,population density,and land use.A household survey of 1377 residents was conducted to validate the model output.High trip generation zones were found in Nyamirambo and Kinyinya,while Nyarugenge,Remera,and Kimironko emerged as strong trip attraction areas.Congestion hotspots were identified at the Muhima,Remera,and Nyabugogo intersections.GIS analysis revealed high biking potential in Kinyinya,Kimironko,and Gatsata,aligning with survey responses.The study proposes 187 new bike-sharing stations in high-priority congestion zones and integrates 19 existing stations to strengthen multimodal connectivity,along with a first and last mile solution.Additionally,15 key employment and service zones covering 67 km were identified to support efficient travel routes.By reducing the need for petrol-engine vehicle rebalancing,the optimized bike-sharing network supports environmental sustainability in the city.The integration of GIS and transport modeling offers a scalable,evidence-based framework for active mobility planning in Kigali and other Sub-Saharan cities in similar conditions to Kigali city in Rwanda.展开更多
There has been increasing interests in developing land use models for small urban areas for various planning applications such as air quality conformity analysis. The output of a land use model can serve as a major in...There has been increasing interests in developing land use models for small urban areas for various planning applications such as air quality conformity analysis. The output of a land use model can serve as a major input to a transportation model; conversely, transportation model output can provide a critical input to a land use model. The connection between the two models can be achieved by an accessibility measure. This paper presents an iterative approach to solving a regression-based land use model and a transportation model with combined trip distribution- assignment. A case study using data from a small urban area is presented to illustrate the application of the proposed modeling framework. Tests show that the procedures can converge, and the modeling framework can be a valuable tool for planners and decision-makers in evaluating land use policies and transportation investment strategies.展开更多
Since the adoption rate of e-grocery skyrocketed in the wake of the Covid-19 pandemic due to the influx of first-time e-grocery shoppers,grocery shopping behavior has been evolving and the travel effects of e-grocery ...Since the adoption rate of e-grocery skyrocketed in the wake of the Covid-19 pandemic due to the influx of first-time e-grocery shoppers,grocery shopping behavior has been evolving and the travel effects of e-grocery are largely unknown.Thus,this study sought to examine the relationship between consumers’grocery shopping behavior online and in-store,and the influencing factors(i.e.,socio-demographic characteristics,household attributes,and personal attitudes).To achieve this,information relating to online and in-store grocery pur chase frequencies,personal and household characteristics,and attitudes of more than 2,000 Florida residents were collected through an online survey.Using a bi-directional structural equation modeling(SEM)approach,our results show that online grocery shop ping exhibited no significant effect on in-store grocery shopping frequency(i.e.,neutrality),but in-store grocery shopping reduced the frequency of online grocery shopping(i.e.,sub stitution).Also,a positive attitude toward some positive aspects of online shopping,pref erence for alternative travel modes,and tech savviness were associated with more frequent online grocery shopping,while cost consciousness and the joy of shopping encouraged more in-store shopping.Several socio-demographic and household attributes were also found to have direct and indirect effects mediated via attitudes on the shopping frequen cies.Overall,this study provides insights into the demand and travel effects of e-grocery and highlights the need for retailers and transport planners to collaborate in order to mit igate the potential travel effects of e-grocery.展开更多
文摘In this study, we examine the impacts that EVs （electric vehicles） have on vehicle usage patterns and environmental improvements, using our integrated travel demand forecasting model, which can simulate an individual activity-travel behavior in each time period, as well as consider an induced demand by decreasing travel cost. In order to examine the effects that charging/discharging have on the demand in electricity, we analyze scenarios based on the simulation results of the EVs＇ parking location, parking duration and the battery state of charge. From the simulation, result under the ownership rate of EVs in the Nagoya metropolitan area in 2020 is about 6%, which turns out that the total CO2 emissions have decreased by 4% although the situation of urban transport is not changed. After calculating the electricity demand in each zone using architectural area and basic units of hourly power consumption, we evaluate the effect to decrease the peak load by V2G （vehicle-to-grid）. According to the results, if EV drivers charge at home during the night and discharge at work during the day, the electricity demand in Nagoya city increases by approximately 1%, although changes in each individual zone range from -7% to ＋8%, depending on its characteristics.
文摘Promoting sustainable mobility and understanding travel demand are critical for rapidly growing cities like Kigali.This research aims to address limitations of traditional transport models by integrating geospatial analysis to support multimodal planning and optimize bike-sharing infrastructure.The study combines the Four-Step Transport Model with Geographic Information Systems(GIS)to enhance spatial disaggregation and identify optimal bike-sharing station locations.It incorporates shortest-path analysis and accounts for topography,road networks,population density,and land use.A household survey of 1377 residents was conducted to validate the model output.High trip generation zones were found in Nyamirambo and Kinyinya,while Nyarugenge,Remera,and Kimironko emerged as strong trip attraction areas.Congestion hotspots were identified at the Muhima,Remera,and Nyabugogo intersections.GIS analysis revealed high biking potential in Kinyinya,Kimironko,and Gatsata,aligning with survey responses.The study proposes 187 new bike-sharing stations in high-priority congestion zones and integrates 19 existing stations to strengthen multimodal connectivity,along with a first and last mile solution.Additionally,15 key employment and service zones covering 67 km were identified to support efficient travel routes.By reducing the need for petrol-engine vehicle rebalancing,the optimized bike-sharing network supports environmental sustainability in the city.The integration of GIS and transport modeling offers a scalable,evidence-based framework for active mobility planning in Kigali and other Sub-Saharan cities in similar conditions to Kigali city in Rwanda.
文摘There has been increasing interests in developing land use models for small urban areas for various planning applications such as air quality conformity analysis. The output of a land use model can serve as a major input to a transportation model; conversely, transportation model output can provide a critical input to a land use model. The connection between the two models can be achieved by an accessibility measure. This paper presents an iterative approach to solving a regression-based land use model and a transportation model with combined trip distribution- assignment. A case study using data from a small urban area is presented to illustrate the application of the proposed modeling framework. Tests show that the procedures can converge, and the modeling framework can be a valuable tool for planners and decision-makers in evaluating land use policies and transportation investment strategies.
文摘Since the adoption rate of e-grocery skyrocketed in the wake of the Covid-19 pandemic due to the influx of first-time e-grocery shoppers,grocery shopping behavior has been evolving and the travel effects of e-grocery are largely unknown.Thus,this study sought to examine the relationship between consumers’grocery shopping behavior online and in-store,and the influencing factors(i.e.,socio-demographic characteristics,household attributes,and personal attitudes).To achieve this,information relating to online and in-store grocery pur chase frequencies,personal and household characteristics,and attitudes of more than 2,000 Florida residents were collected through an online survey.Using a bi-directional structural equation modeling(SEM)approach,our results show that online grocery shop ping exhibited no significant effect on in-store grocery shopping frequency(i.e.,neutrality),but in-store grocery shopping reduced the frequency of online grocery shopping(i.e.,sub stitution).Also,a positive attitude toward some positive aspects of online shopping,pref erence for alternative travel modes,and tech savviness were associated with more frequent online grocery shopping,while cost consciousness and the joy of shopping encouraged more in-store shopping.Several socio-demographic and household attributes were also found to have direct and indirect effects mediated via attitudes on the shopping frequen cies.Overall,this study provides insights into the demand and travel effects of e-grocery and highlights the need for retailers and transport planners to collaborate in order to mit igate the potential travel effects of e-grocery.
