This study proposes a prediction model considering external weather and holiday factors to address the issue of accurately predicting urban taxi travel demand caused by complex data and numerous influencing factors.Th...This study proposes a prediction model considering external weather and holiday factors to address the issue of accurately predicting urban taxi travel demand caused by complex data and numerous influencing factors.The model integrates the Complete Ensemble Empirical Mode Decomposition with Adaptive Noise(CEEMDAN)and Convolutional Long Short Term Memory Neural Network(ConvLSTM)to predict short-term taxi travel demand.The CEEMDAN decomposition method effectively decomposes time series data into a set of modal components,capturing sequence characteristics at different time scales and frequencies.Based on the sample entropy value of components,secondary processing of more complex sequence components after decomposition is employed to reduce the cumulative prediction error of component sequences and improve prediction efficiency.On this basis,considering the correlation between the spatiotemporal trends of short-term taxi traffic,a ConvLSTM neural network model with Long Short Term Memory(LSTM)time series processing ability and Convolutional Neural Networks(CNN)spatial feature processing ability is constructed to predict the travel demand for urban taxis.The combined prediction model is tested on a taxi travel demand dataset in a certain area of Beijing.The results show that the CEEMDAN-ConvLSTM prediction model outperforms the LSTM,Autoregressive Integrated Moving Average model(ARIMA),CNN,and ConvLSTM benchmark models in terms of Symmetric Mean Absolute Percentage Error(SMAPE),Root Mean Square Error(RMSE),Mean Absolute Error(MAE),and R2 metrics.Notably,the SMAPE metric exhibits a remarkable decline of 21.03%with the utilization of our proposed model.These results confirm that our study provides a highly accurate and valid model for taxi travel demand forecasting.展开更多
In view of the problem that the requirements of travel demand management and traffic policy-sensitivity are ignored during the establishment process of the travel demand forecasting model, a discrete-choice-based trav...In view of the problem that the requirements of travel demand management and traffic policy-sensitivity are ignored during the establishment process of the travel demand forecasting model, a discrete-choice-based travel demand forecasting model is proposed to demonstrate its applicability to travel demand management. A car-bus discrete choice model is established, including three variables, i. e,, individual socioeconomic characteristics, time, and cost, and the traffic policy-sensitivity is evaluated through two kinds of traffic policies： parking charges and bus priorities. The empirical results show that travel choice is insensitive to the policy of parking charges as 88. 41% of the travelers are insensitive to parking charges; travel choice is, however, sensitive to the policy of bus priorities as 67.70% of the car travelers and 77.02% of the bus travelers are sensitive to bus priorities. The discrete-choice-based travel demand forecasting model is quite policy-sensitive and also has a good adaptability for travel demand management when meeting the basic functions of the demand forecasting model.展开更多
Traffic congestion has become a critical issue in developing countries,as it tends to increase social costs in terms of travel cost and time,energy consumption and environmental degradation.With limited resources,redu...Traffic congestion has become a critical issue in developing countries,as it tends to increase social costs in terms of travel cost and time,energy consumption and environmental degradation.With limited resources,reducing travel demand by influencing individuals’ travel behavior can be a better long-term solution.To achieve this objective,alternate travel options need to be provided so that people can commute comfortably and economically.This study aims to identify key motives and constraints in the consideration of carpooling policy with the help of stated preference questionnaire survey that was conducted in Lahore City.The designed questionnaire includes respondents’ socioeconomic demographics,and intentions and stated preferences on carpooling policy.Factor analysis was conducted on travelers’ responses,and a structural model was developed for carpooling.Survey and modeling results reveal that social,environmental and economic benefits,disincentives on car use,preferential parking treatment for carpooling,and comfort and convenience attributes are significant determinants in promoting carpooling.However,people with strong belief in personal privacy,security,freedom in traveling and carpooling service constraints would have less potential to use thecarpooling service.In addition,pro-auto and pro-carpooling attitudes,marital status,profession and travel purpose for carpooling are also underlying factors.The findings implicate that to promote carpooling policy it is required to consider appropriate incentives on this service and disincentives on use of private vehicle along with modification of people’s attitudes and intentions.展开更多
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
As a megacity with thriving economy, Shanghai is experiencing rapid motorisation and confronted with traffic congestion problems despite its low car ownership. It is of value to look into the policies on emission cont...As a megacity with thriving economy, Shanghai is experiencing rapid motorisation and confronted with traffic congestion problems despite its low car ownership. It is of value to look into the policies on emission control of motor vehicle and congestion reduction in such a city to explore how to reconcile mobility enhancement with the environment. Results of a dynamic simulation displayed time paths of emissions from motor vehicles in Shanghai over the period from 2000 to 2020. The simulation results showed that early policies on emission control of motor vehicle could bring about far-reaching effects on emission reduc- tion, and take advantage of available low-polluting technologies and technical innovation over time. Travel demand management would play an important role in curbing congestion and reducing motor vehicle pollution by calming down car ownership rise and deterring inefficient trips as well as reducing fuel waste caused by congestion.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
Congestion causes many externalities for the society, including time delays, excessive fuel consumption, air pollution, noise and safety concerns. In Shanghai, various policy options have been explored, piloted or app...Congestion causes many externalities for the society, including time delays, excessive fuel consumption, air pollution, noise and safety concerns. In Shanghai, various policy options have been explored, piloted or applied; however, not all of them may be understood and accepted by the public. A survey was conducted to investigate people＇s attitudes towards several policy options. The main findings reveal that Shanghai residents are resistant to certain policies, such as congestion charges, higher parking charges in congested areas and car restrictions. Instead, they favor public transport provisions. The paper suggests that there is a case for promoting public transport and more efficient trips when the car ownership is still low, and for investing in a policy of educating the public on the ＇true＇ costs and causes of congestion before embarking on ,an intensive policy of congestion charges or restrictions.展开更多
In the last decades, there has been substantial development in modeling techniques of travel demand estimation. For low population areas the external trip estimation is important but usually neglected in travel demand...In the last decades, there has been substantial development in modeling techniques of travel demand estimation. For low population areas the external trip estimation is important but usually neglected in travel demand modeling process. In Egypt, the researches in this field are scarce due to lack of data. Accordingly, this paper aims to identify and estimate the main variables that affect the travel demand in low population areas, and to develop models to predict them. The study focused on the Port Said Govemorate in North East Egypt. A special questionnaire had been prepared in 2010 depending on interviews of passengers at basic taxi terminals in Port Said. And 2211 filled questionnaires were offering for research. To analyze the data, two modeling procedures were used. One is the multiple linear regression and the other is the generalized linear modeling （GLM） applying normal distributions. It is found that GLM procedure offers more suitable and accurate approach than the linear regression for developing number of trips. The final demand models have statistics within the acceptable regions and, also, they are conceptually reasonable. These results are so important for Egyptian highway authorities to improve the efficiency of highway transportation system in Egypt.展开更多
A method is presented in this work that integrates both emerging and mature data sources to estimate the operational travel demand in fine spatial and temporal resolutions.By analyzing individuals’mobility patterns r...A method is presented in this work that integrates both emerging and mature data sources to estimate the operational travel demand in fine spatial and temporal resolutions.By analyzing individuals’mobility patterns revealed from their mobile phones,researchers and practitioners are now equipped to derive the largest trip samples for a region.Because of its ubiquitous use,extensive coverage of telecommunication services and high penetration rates,travel demand can be studied continuously in fine spatial and temporal resolutions.The derived sample or seed trip matrices are coupled with surveyed commute flow data and prevalent travel demand modeling techniques to provide estimates of the total regional travel demand in the form of origindestination(OD)matrices.The methodology is evaluated in a series of real world transportation planning studies and proved its potentials in application areas such as dynamic traffic assignment modeling,integrated corridor management and online traffic simulations.展开更多
“The past two years have been extremelydifficull for the travel and tourism industry-notleast for the hospitality sector,”Alain-PhilippeFeutre.IH&RA CEO told delegates from some40 countries meeting in Cairo for ...“The past two years have been extremelydifficull for the travel and tourism industry-notleast for the hospitality sector,”Alain-PhilippeFeutre.IH&RA CEO told delegates from some40 countries meeting in Cairo for the 40thAnnual Congress of the Internationa J Hotel ＆Restaurant Association(5-9 December 2003).Yet three days of presentations and discussionsin the Egyptian capital reflected a newly foundconfidence among hotel operators,suppliers,destinations and other tourism experts as to theimminent revivaI of travel and tourism demand.展开更多
This paper considers the problem of supply-demand imbalances in Mobility-on-Demand(MoD)services.These imbalances occur due to uneven stochastic travel demand and can be mitigated by proactively rebalancing empty vehic...This paper considers the problem of supply-demand imbalances in Mobility-on-Demand(MoD)services.These imbalances occur due to uneven stochastic travel demand and can be mitigated by proactively rebalancing empty vehicles to areas where the demand is high.To achieve this,we propose a method that takes into account uncertainties of predicted travel demand while minimizing pick-up time and rebalance mileage for autonomous MoD ride-hailing.More precisely,first travel demand is predicted using Gaussian Process Regression(GPR)which provides uncertainty bounds on the prediction.We then formulate a stochastic model predictive control(MPC)for the autonomous ride-hailing service and integrate the demand predictions with uncertainty bounds.In order to guarantee constraint satisfaction in the optimization under estimated stochastic demand prediction,we employ a probabilistic constraining method with user-defined confidence interval,using Chance Constrained MPC(CCMPC).The benefits of the proposed method are twofold.First,travel demand uncertainty prediction from data can naturally be embedded into the MoD optimization framework,allowing us to keep the imbalance at each station below a certain threshold with a user-defined probability.Second,CCMPC can be relaxed into a Mixed-Integer-Linear-Program(MILP)and the MILP can be solved as a corresponding Linear-Program,which always admits an integral solution.Our transportation simulations show that by tuning the confidence bound on the chance constraint,close to optimal oracle performance can be achieved,with a median customer wait time reduction of 4%compared to using only the mean prediction of the GPR.展开更多
Studies in transportation planning routinely use data in which location attributes are an important source of information.Thus,using spatial attributes in urban travel forecasting models seems reasonable.The main obje...Studies in transportation planning routinely use data in which location attributes are an important source of information.Thus,using spatial attributes in urban travel forecasting models seems reasonable.The main objective of this paper is to estimate transit trip production using Factorial Kriging with External Drift(FKED)through an aggregated data case study of Traffic Analysis Zones in São Paulo city,Brazil.The method consists of a sequential application of Principal Components Analysis(PCA)and Kriging with External Drift(KED).The traditional Linear Regression(LR)model was adopted with the aim of validating the proposed method.The results show that PCA summarizes and combines 23 socioeconomic variables using 4 components.The first component is introduced in KED,as secondary information,to estimate transit trip production by public transport in geographic coordinates where there is no prior knowledge of the values.Cross-validation for the FKED model presented high values of the correlation coefficient between estimated and observed values.Moreover,low error values were observed.The accuracy of the LR model was similar to FKED.However,the proposed method is able to map the transit trip production in several geographical coordinates of non-sampled values.展开更多
基金supported by the Surface Project of the National Natural Science Foundation of China(No.71273024)the Fundamental Research Funds for the Central Universities of China(2021YJS080).
文摘This study proposes a prediction model considering external weather and holiday factors to address the issue of accurately predicting urban taxi travel demand caused by complex data and numerous influencing factors.The model integrates the Complete Ensemble Empirical Mode Decomposition with Adaptive Noise(CEEMDAN)and Convolutional Long Short Term Memory Neural Network(ConvLSTM)to predict short-term taxi travel demand.The CEEMDAN decomposition method effectively decomposes time series data into a set of modal components,capturing sequence characteristics at different time scales and frequencies.Based on the sample entropy value of components,secondary processing of more complex sequence components after decomposition is employed to reduce the cumulative prediction error of component sequences and improve prediction efficiency.On this basis,considering the correlation between the spatiotemporal trends of short-term taxi traffic,a ConvLSTM neural network model with Long Short Term Memory(LSTM)time series processing ability and Convolutional Neural Networks(CNN)spatial feature processing ability is constructed to predict the travel demand for urban taxis.The combined prediction model is tested on a taxi travel demand dataset in a certain area of Beijing.The results show that the CEEMDAN-ConvLSTM prediction model outperforms the LSTM,Autoregressive Integrated Moving Average model(ARIMA),CNN,and ConvLSTM benchmark models in terms of Symmetric Mean Absolute Percentage Error(SMAPE),Root Mean Square Error(RMSE),Mean Absolute Error(MAE),and R2 metrics.Notably,the SMAPE metric exhibits a remarkable decline of 21.03%with the utilization of our proposed model.These results confirm that our study provides a highly accurate and valid model for taxi travel demand forecasting.
文摘In view of the problem that the requirements of travel demand management and traffic policy-sensitivity are ignored during the establishment process of the travel demand forecasting model, a discrete-choice-based travel demand forecasting model is proposed to demonstrate its applicability to travel demand management. A car-bus discrete choice model is established, including three variables, i. e,, individual socioeconomic characteristics, time, and cost, and the traffic policy-sensitivity is evaluated through two kinds of traffic policies： parking charges and bus priorities. The empirical results show that travel choice is insensitive to the policy of parking charges as 88. 41% of the travelers are insensitive to parking charges; travel choice is, however, sensitive to the policy of bus priorities as 67.70% of the car travelers and 77.02% of the bus travelers are sensitive to bus priorities. The discrete-choice-based travel demand forecasting model is quite policy-sensitive and also has a good adaptability for travel demand management when meeting the basic functions of the demand forecasting model.
基金conducted at University of Engineering and Technology Lahore with support of Department of Transportation Engineering and Management Department
文摘Traffic congestion has become a critical issue in developing countries,as it tends to increase social costs in terms of travel cost and time,energy consumption and environmental degradation.With limited resources,reducing travel demand by influencing individuals’ travel behavior can be a better long-term solution.To achieve this objective,alternate travel options need to be provided so that people can commute comfortably and economically.This study aims to identify key motives and constraints in the consideration of carpooling policy with the help of stated preference questionnaire survey that was conducted in Lahore City.The designed questionnaire includes respondents’ socioeconomic demographics,and intentions and stated preferences on carpooling policy.Factor analysis was conducted on travelers’ responses,and a structural model was developed for carpooling.Survey and modeling results reveal that social,environmental and economic benefits,disincentives on car use,preferential parking treatment for carpooling,and comfort and convenience attributes are significant determinants in promoting carpooling.However,people with strong belief in personal privacy,security,freedom in traveling and carpooling service constraints would have less potential to use thecarpooling service.In addition,pro-auto and pro-carpooling attitudes,marital status,profession and travel purpose for carpooling are also underlying factors.The findings implicate that to promote carpooling policy it is required to consider appropriate incentives on this service and disincentives on use of private vehicle along with modification of people’s attitudes and intentions.
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As a megacity with thriving economy, Shanghai is experiencing rapid motorisation and confronted with traffic congestion problems despite its low car ownership. It is of value to look into the policies on emission control of motor vehicle and congestion reduction in such a city to explore how to reconcile mobility enhancement with the environment. Results of a dynamic simulation displayed time paths of emissions from motor vehicles in Shanghai over the period from 2000 to 2020. The simulation results showed that early policies on emission control of motor vehicle could bring about far-reaching effects on emission reduc- tion, and take advantage of available low-polluting technologies and technical innovation over time. Travel demand management would play an important role in curbing congestion and reducing motor vehicle pollution by calming down car ownership rise and deterring inefficient trips as well as reducing fuel waste caused by congestion.
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Congestion causes many externalities for the society, including time delays, excessive fuel consumption, air pollution, noise and safety concerns. In Shanghai, various policy options have been explored, piloted or applied; however, not all of them may be understood and accepted by the public. A survey was conducted to investigate people＇s attitudes towards several policy options. The main findings reveal that Shanghai residents are resistant to certain policies, such as congestion charges, higher parking charges in congested areas and car restrictions. Instead, they favor public transport provisions. The paper suggests that there is a case for promoting public transport and more efficient trips when the car ownership is still low, and for investing in a policy of educating the public on the ＇true＇ costs and causes of congestion before embarking on ,an intensive policy of congestion charges or restrictions.
文摘In the last decades, there has been substantial development in modeling techniques of travel demand estimation. For low population areas the external trip estimation is important but usually neglected in travel demand modeling process. In Egypt, the researches in this field are scarce due to lack of data. Accordingly, this paper aims to identify and estimate the main variables that affect the travel demand in low population areas, and to develop models to predict them. The study focused on the Port Said Govemorate in North East Egypt. A special questionnaire had been prepared in 2010 depending on interviews of passengers at basic taxi terminals in Port Said. And 2211 filled questionnaires were offering for research. To analyze the data, two modeling procedures were used. One is the multiple linear regression and the other is the generalized linear modeling （GLM） applying normal distributions. It is found that GLM procedure offers more suitable and accurate approach than the linear regression for developing number of trips. The final demand models have statistics within the acceptable regions and, also, they are conceptually reasonable. These results are so important for Egyptian highway authorities to improve the efficiency of highway transportation system in Egypt.
文摘A method is presented in this work that integrates both emerging and mature data sources to estimate the operational travel demand in fine spatial and temporal resolutions.By analyzing individuals’mobility patterns revealed from their mobile phones,researchers and practitioners are now equipped to derive the largest trip samples for a region.Because of its ubiquitous use,extensive coverage of telecommunication services and high penetration rates,travel demand can be studied continuously in fine spatial and temporal resolutions.The derived sample or seed trip matrices are coupled with surveyed commute flow data and prevalent travel demand modeling techniques to provide estimates of the total regional travel demand in the form of origindestination(OD)matrices.The methodology is evaluated in a series of real world transportation planning studies and proved its potentials in application areas such as dynamic traffic assignment modeling,integrated corridor management and online traffic simulations.
文摘“The past two years have been extremelydifficull for the travel and tourism industry-notleast for the hospitality sector,”Alain-PhilippeFeutre.IH&RA CEO told delegates from some40 countries meeting in Cairo for the 40thAnnual Congress of the Internationa J Hotel ＆Restaurant Association(5-9 December 2003).Yet three days of presentations and discussionsin the Egyptian capital reflected a newly foundconfidence among hotel operators,suppliers,destinations and other tourism experts as to theimminent revivaI of travel and tourism demand.
基金co-funded by Vinnova,Sweden through the project:Simulation,analysis and modeling of future efficient traffic systems.
文摘This paper considers the problem of supply-demand imbalances in Mobility-on-Demand(MoD)services.These imbalances occur due to uneven stochastic travel demand and can be mitigated by proactively rebalancing empty vehicles to areas where the demand is high.To achieve this,we propose a method that takes into account uncertainties of predicted travel demand while minimizing pick-up time and rebalance mileage for autonomous MoD ride-hailing.More precisely,first travel demand is predicted using Gaussian Process Regression(GPR)which provides uncertainty bounds on the prediction.We then formulate a stochastic model predictive control(MPC)for the autonomous ride-hailing service and integrate the demand predictions with uncertainty bounds.In order to guarantee constraint satisfaction in the optimization under estimated stochastic demand prediction,we employ a probabilistic constraining method with user-defined confidence interval,using Chance Constrained MPC(CCMPC).The benefits of the proposed method are twofold.First,travel demand uncertainty prediction from data can naturally be embedded into the MoD optimization framework,allowing us to keep the imbalance at each station below a certain threshold with a user-defined probability.Second,CCMPC can be relaxed into a Mixed-Integer-Linear-Program(MILP)and the MILP can be solved as a corresponding Linear-Program,which always admits an integral solution.Our transportation simulations show that by tuning the confidence bound on the chance constraint,close to optimal oracle performance can be achieved,with a median customer wait time reduction of 4%compared to using only the mean prediction of the GPR.
基金This research was sponsored by the National Counsel of Technological and Scientific Development(CNPq,Brazil),the Coordination for the Improvement of Higher Education Personnel(CAPES,Brazil),the State of São Paulo Research Foundation(FAPESP-2013/25035-1,Brazil).
文摘Studies in transportation planning routinely use data in which location attributes are an important source of information.Thus,using spatial attributes in urban travel forecasting models seems reasonable.The main objective of this paper is to estimate transit trip production using Factorial Kriging with External Drift(FKED)through an aggregated data case study of Traffic Analysis Zones in São Paulo city,Brazil.The method consists of a sequential application of Principal Components Analysis(PCA)and Kriging with External Drift(KED).The traditional Linear Regression(LR)model was adopted with the aim of validating the proposed method.The results show that PCA summarizes and combines 23 socioeconomic variables using 4 components.The first component is introduced in KED,as secondary information,to estimate transit trip production by public transport in geographic coordinates where there is no prior knowledge of the values.Cross-validation for the FKED model presented high values of the correlation coefficient between estimated and observed values.Moreover,low error values were observed.The accuracy of the LR model was similar to FKED.However,the proposed method is able to map the transit trip production in several geographical coordinates of non-sampled values.
