基于出租車承載行為的優(yōu)化決策調(diào)度方法研究
發(fā)布時(shí)間：2018-09-08 15:38
【摘要】：出租汽車作為城市公共交通重要的出行方式,為居民提供快捷、高效的出行服務(wù),隨著城市的快速發(fā)展和居民生活水平的提高,出租車行業(yè)面臨著新的挑戰(zhàn):承包經(jīng)營模式高額承包費(fèi)弊端、出租車運(yùn)營空駛率高、乘客要求更便捷的出行服務(wù)等。更加迫切的需要通過有效的出租車調(diào)度方法來提高出行服務(wù)質(zhì)量。本文通過對出租車承載行為的分析,提出了基于載客熱點(diǎn)的出租車調(diào)度方法,來提高城市出租車調(diào)度管理系統(tǒng)的科學(xué)、有效、合理性,提升市民出行的便捷度及出租車公司的運(yùn)營效益;降低城市出租車的空駛率及運(yùn)營成本;減少城市道路交通負(fù)荷及因乘客滯留導(dǎo)致的公共安全危害。通過對出租車服務(wù)特性、承載行為特性和調(diào)度特性的分析,了解其承載行為在時(shí)間上的特性指標(biāo)有出行次數(shù)、單次出行耗時(shí)和時(shí)間空駛率,在空間上的特性指標(biāo)是出行需求分布、出行距離分布、出行路徑偏好。通過統(tǒng)計(jì)出租車回傳至信息中心的出租車位置、速度、載客狀態(tài)等數(shù)據(jù),進(jìn)一步分析路網(wǎng)運(yùn)行狀況和出租車服務(wù)情況;并對數(shù)據(jù)采集誤差分析,通過SQL數(shù)據(jù)庫剔除冗余數(shù)據(jù)和錯(cuò)誤數(shù)據(jù);對電子地圖進(jìn)行拓?fù)涮幚?如清除微短線、弧段重疊坐標(biāo),刪除懸掛弧段,坐標(biāo)轉(zhuǎn)換等;由于GPS數(shù)據(jù)和GIS地圖數(shù)據(jù)都存在誤差,懫用最短距離算法將出租車行駛軌跡數(shù)據(jù)匹配到GIS路網(wǎng)數(shù)據(jù)上。通過對南京市出租車GPS數(shù)據(jù)的處理,獲得南京市出租承載行為時(shí)間和空間特征為,出租車GPS數(shù)據(jù)在空間上具有聚類特征,選用基于劃分的K-means聚類算法,并通過Weka數(shù)據(jù)挖掘平臺(tái)計(jì)算聚類結(jié)果,得到不同時(shí)間段的出租車上下客聚類中心。介紹了目前常用的出租車調(diào)度方法有階梯調(diào)度和響應(yīng)乘客信息的調(diào)度方法,前者過于粗略且不能提供出租車有效行駛路徑,后者是完全從乘客角度出發(fā),二者都忽略了空載出租車的需求。本文提出了從出租車司機(jī)角度,在空載行駛時(shí)向調(diào)度中心請求調(diào)度的方法,該方法是基于乘客的出行熱點(diǎn),然后對載客熱點(diǎn)區(qū)域的出租車適應(yīng)度進(jìn)行評價(jià),主要評價(jià)指標(biāo)是出租車的飽和率和路網(wǎng)負(fù)荷度,在載客熱點(diǎn)區(qū)域的適應(yīng)度滿足相應(yīng)條件時(shí)建議出租車調(diào)往該區(qū)域,并通過Dijkstra算法計(jì)算出最優(yōu)巡游路徑。
[Abstract]:As an important way of urban public transportation, taxi provides residents with fast and efficient travel services, with the rapid development of the city and the improvement of living standards. The taxi industry is faced with new challenges: the malpractice of high contracting fee in contract management mode, the high empty driving rate of taxi operation, and the passengers' request for more convenient travel service, etc. More urgent need to improve the quality of travel services through effective taxi scheduling methods. Based on the analysis of taxi carrying behavior, this paper puts forward a taxi dispatching method based on hot spot to improve the science, efficiency and rationality of urban taxi dispatching management system. To improve the convenience of public travel and the operating efficiency of taxi companies; to reduce the empty driving rate and operating costs of urban taxis; to reduce the urban road traffic load and public safety hazards caused by passenger detention. By analyzing the characteristics of taxi service, load bearing behavior and scheduling, we can find out that the characteristic indexes of carrying behavior in time include travel times, time consuming and time empty driving rate. The characteristic indexes in space are travel demand distribution, travel distance distribution and travel path preference. Through the statistics of taxi position, speed, passenger status and so on, the running status of the network and taxi service are further analyzed, and the error of data collection is analyzed. The redundant data and error data are eliminated by SQL database, and the electronic map is topologically processed, such as removing microshort lines, overlapping coordinates of arcs, deleting suspended arcs, coordinate conversion, etc., because of errors in GPS data and GIS map data. The shortest distance algorithm is used to match the taxi track data to the GIS road network data. By processing the GPS data of taxis in Nanjing, the time and space characteristics of the bearing behavior of taxi in Nanjing are obtained. The GPS data of taxis have clustering characteristics in space, and the K-means clustering algorithm based on partition is chosen. The clustering results are calculated by Weka data mining platform, and the taxicab cluster center is obtained in different time periods. This paper introduces the common methods of taxi scheduling at present, that is, step dispatching and response to passenger information. The former is too rough to provide an effective taxi route, and the latter is entirely from the passenger's point of view. Both ignore the demand for empty taxis. This paper presents a method of requesting dispatch from the dispatching center from the taxi driver's point of view. The method is based on the passenger's hot spot, and then evaluates the taxi fitness in the hot spot area. The main evaluation index is the saturation rate of taxi and the load degree of the road network. When the fitness of the hot passenger region meets the corresponding conditions, it is suggested that the taxi should be transferred to the region, and the optimal cruise path can be calculated by the Dijkstra algorithm.
【學(xué)位授予單位】：重慶交通大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2015
【分類號(hào)】：U492.434
本文編號(hào)：2230949
[Abstract]:As an important way of urban public transportation, taxi provides residents with fast and efficient travel services, with the rapid development of the city and the improvement of living standards. The taxi industry is faced with new challenges: the malpractice of high contracting fee in contract management mode, the high empty driving rate of taxi operation, and the passengers' request for more convenient travel service, etc. More urgent need to improve the quality of travel services through effective taxi scheduling methods. Based on the analysis of taxi carrying behavior, this paper puts forward a taxi dispatching method based on hot spot to improve the science, efficiency and rationality of urban taxi dispatching management system. To improve the convenience of public travel and the operating efficiency of taxi companies; to reduce the empty driving rate and operating costs of urban taxis; to reduce the urban road traffic load and public safety hazards caused by passenger detention. By analyzing the characteristics of taxi service, load bearing behavior and scheduling, we can find out that the characteristic indexes of carrying behavior in time include travel times, time consuming and time empty driving rate. The characteristic indexes in space are travel demand distribution, travel distance distribution and travel path preference. Through the statistics of taxi position, speed, passenger status and so on, the running status of the network and taxi service are further analyzed, and the error of data collection is analyzed. The redundant data and error data are eliminated by SQL database, and the electronic map is topologically processed, such as removing microshort lines, overlapping coordinates of arcs, deleting suspended arcs, coordinate conversion, etc., because of errors in GPS data and GIS map data. The shortest distance algorithm is used to match the taxi track data to the GIS road network data. By processing the GPS data of taxis in Nanjing, the time and space characteristics of the bearing behavior of taxi in Nanjing are obtained. The GPS data of taxis have clustering characteristics in space, and the K-means clustering algorithm based on partition is chosen. The clustering results are calculated by Weka data mining platform, and the taxicab cluster center is obtained in different time periods. This paper introduces the common methods of taxi scheduling at present, that is, step dispatching and response to passenger information. The former is too rough to provide an effective taxi route, and the latter is entirely from the passenger's point of view. Both ignore the demand for empty taxis. This paper presents a method of requesting dispatch from the dispatching center from the taxi driver's point of view. The method is based on the passenger's hot spot, and then evaluates the taxi fitness in the hot spot area. The main evaluation index is the saturation rate of taxi and the load degree of the road network. When the fitness of the hot passenger region meets the corresponding conditions, it is suggested that the taxi should be transferred to the region, and the optimal cruise path can be calculated by the Dijkstra algorithm.
【學(xué)位授予單位】：重慶交通大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2015
【分類號(hào)】：U492.434
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，本文編號(hào)：2230949
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