Methods For Describing The Travel Characteristics And Optimizing The Operation Of Carpooling Under The Scale Of Metropolitan Region

The Mobile-Internet based carpooling services have become popular among urban private car users due to their higher demand response efficiency and lower travel costs.Compared with other urban travel modes,on the one hand,carpooling trips are with longer travel distance and obvious commuting function and their trajectory data can truly reflect the travel characteristics of the residents.Hence,carpooling big data can better describe the complex characteristics of the metropolitan spatial structure and then help design more effective urban traffic and space development strategies.However,there is still lack of empirical research based on carpooling big data at present.On the other hand,carpooling is more flexible than public transit and more energy-saving than private cars driving-alone,which is considered as an effective means to relieve the problem of car dependence.As an emerging sharing travel mode,however,the carpooling service is still with lower market share.There are only a few studies focusing on operation and management of carpooling at present,which still need to be deepened in terms of data use and research scope,energy-saving optimization and matching mode design still.To better play the role of carpooling in alleviating metropolitan traffic problems,from the perspective of coupling relationship of urban traffic organization and spatial structure,this paper described the travel characteristics and optimized the operation of carpooling in the scale of metropolitan region driven by carpooling big data.The former job aims to deeply excavate the travel characteristics of carpooling.The related results can be used to identify the metropolitan polycentric structure,which also lay a foundation for the subsequent analysis on the spatial performance of carpooling energy saving and user matching.The latter includes the formulation of energy saving subsidy and the construction of ridesharing matching model,aiming at designing more effective promotion policies and matching schemes,and then promoting the large-scale development of carpooling services within metropolitan regions.The main research contents and findings include four parts as followings.First,we conducted in-depth mining on the spatio-temporal characteristics,user characteristics,collective characteristics and operation characteristics of carpooling trips based on massive actual operation data.According to the analysis results,we identified the service characteristics of different types of carpooling drivers and established the carpooling's sharing principles of apportioning the trip cost equally and carrying passengers with less detour.More importantly,we demonstrated that carpooling is a kind of ridesharing mode with the service function of commuting within the metropolitan area,from the perspectives of spatio-temporal distribution statistics,map visualization and mathematical processing methods,which can effectively support the identification on metropolitan spatial structure.Second,we proposed a three-step method to identify the metropolitan polycentric spatial structure based on carpooling travel features.The first step is identifying the layout of polycentric spatial structure using the grid-model-based density clustering method.The second step is defining the carpooling service influence scope of metropolitan area based on the superposition relationship between commuting intensity and accessibility of geographic unit to metropolitan center system.The third step is comprehensively evaluating the travel characteristics' spatial performances of the multiple centers by two sets of density indicators and flow indicators.The results of case study show that Beijing Metropolitan Region has the polycentric structure with one primary center and multiple sub-centers.The influence of the metropolitan center system extends beyond the administrative boundaries to the neighboring districts and counties.The multiple sub-centers show obvious heterogeneity in the spatial performance of human activities and play different regional roles including the employment sub-centers,commuter towns and satellite towns.The associated results and findings can help design more effective land-use and transportation planning strategies in metropolitan regions.Third,we proposed the trip-specific fuel saving estimation method and the subsidy formulation method for carpooling trips in metropolitan regions.According to the travel distances and fuel economy parameters under different passenger load factor scenarios,we estimated the fuel savings of common carpooling mode and four secondary carpooling modes.Moreover,under the Personal Carbon Trading scheme,we designed the fuel saving oriented subsidy policy based on the principles of moderate subsidy and equal distribution for carpooling trips considering different pricing scenarios.According to the case study,we found that the common carpooling trip within the city is the main carpooling mode in Beijing Metropolitan Region,while the secondary carpooling mode and intercity travel mode have higher fuel saving efficiency.The fuel saving level of a carpooling trip is affected by the travel distance and detour distance.The carpooling trips with shorter travel distance and longer detour distance can consume more fuel.The fuel savings of carpooling trips are mainly generated from the metropolitan primary center,while the carpooling trips from or to outer sub-centers have higher fuel saving efficiency.The distribution of carpooling fuel savings on weekdays has obvious characteristics of morning and evening peak.The fuel saving oriented subsidy schemes under different pricing scenarios can moderately and effectively promote carpooling services.Lastly,a passenger-to-driver ridesharing matching model was established for private car trips in metropolitan regions.We proposed the passenger-to-driver matching process based on online carpooling platform and estimated the generalized travel cost including the psychological cost of sharing trip and the psychological benefit of environmental protection.The matching model is formulated as a 0-1 integer linear programming problem and then is used to conduct sensitivity analysis on the key operational issues such as initial user participant and flexibility level,cost-sharing rate and shared penalty cost,as well as the promotion policy.Based on the case study of the Great London,it is found that carpooling trips mainly formed in the outside employment sub-centers of this metropolitan region,while the matching success rate is higher within the congestion charging area of the primary center.The matching performances of various sharing modes are obviously different.User flexibility can significantly improve the matching performance of the carpooling system,especially when the initial participation rates are relatively low.The optimal cost-sharing rate should be carpooling passenger paying a lower proportion.Punishment measures such as the higher parking charges are more effective to improve the matching success rate.The limited fuel saving subsidy can effectively improve the performance of carpooling matching and the level of fuel savings and emission reductions,which can further guide the current carpooling mode developing into a sharing-type carpooling mode with longer sharing distance,lower level of detour,and prominent benefit of fuel savings and emission reductions.This study first based on the carpooling big data,established a series of innovative models and then formed the optimization strategies coupling metropolitan traffic organization and spatial structure,which has important theoretical value and practical guiding significance for the sustainable development of carpooling and even the metropolitan traffic system.