需求响应式公交到站时间预测研究
|
摘要
针对城市绵延情况下城市新区公交低密度地区的交通出行问题,开展对实时响应模式下的需求响应式公交的到站时间预测研究。以时间序列法和BP神经网络模型为基础,提出综合预测模型,并用最小二乘法对模型求解。证实了在城市新区开通需求响应式公交服务模式的可行性,以及为居民提供出行便捷、合理约束到站时间的公共交通服务的能力,为需求响应式公交服务模式的推行起到积极作用。
In order to solve the problem of bus arrival time in low-density areas of new urban districts, this paper carries out research on the arrival time prediction of demand-respon-sive bus under real-time response mode. In the demand-responsive bus-to-station forecasting stage, this paper proposes a comprehensive forecasting model based on two classical forecasting models, the time series method and the BP neural network model, and uses the least squares method to solve the model. The final results of this paper confirm the feasibility of opening a demand-responsive bus service model in the new urban area, and provide residents with the ability to travel public transportation services that are convenient and reasonably constrained for arrival time. It has played an active role in implementing the demand-responsive bus service model.
In order to solve the problem of bus arrival time in low-density areas of new urban districts, this paper carries out research on the arrival time prediction of demand-respon-sive bus under real-time response mode. In the demand-responsive bus-to-station forecasting stage, this paper proposes a comprehensive forecasting model based on two classical forecasting models, the time series method and the BP neural network model, and uses the least squares method to solve the model. The final results of this paper confirm the feasibility of opening a demand-responsive bus service model in the new urban area, and provide residents with the ability to travel public transportation services that are convenient and reasonably constrained for arrival time. It has played an active role in implementing the demand-responsive bus service model.
引文
[1]魏天娇,周磊山.基于关键客流密度选择支路公交运营模式的方法[J].交通运输研究,2012(12):43-47.
[2]高煦明.固定站点需求响应式接驳公交调度模型研究[D].长沙:东南大学(硕士学位论文),2015.
[3]王颖.需求响应型公共交通系统框架设计及适应性评价关键技术研究[D].济南:山东大学(硕士学位论文),2012.
[4]邱丰.可变线路式公交运营调度与模式优化研究[D].长沙:东南大学(博士学位论文),2015.
[5] Alshalalfah B, Shalaby A. Feasibility of Flex-Route as a Feeder Transit Service to Rail Stations in the Suburbs:Case Study in Toronto[J]. Journal of Urban Planning&Development, 2012,138(1):90-100.
[6] Patnaik J, Chien S, Bladikas A. Estimation of Bus Arrival Times Using APC Data[J]. Physical Review Letters, 2010,7(1):128001-128100.
[7]周雪梅,杨晓光,王磊.公交车辆行程时间预测方法研究[J].交通信息与安全,2002,20(6):12-14.
[8]陈坚,李武,吴丹,等.基于MAST的智慧公交系统调度优化模型[J].重庆交大学学报(自然科学版),2016(5):140-145.
[9] Shalaby A, Eng P, Farhan A, et al. Bus travel time prediction model for dynamic operations control and passenger information systems[J]. Steroids, 2002,53(1-2):131-147.
[10]徐荣荣.需求响应型公交在农村客运中的应用研究[D].西安:长安大学(硕士学位论文),2015.
[11]杨敏,丁剑,王炜.基于ARIMA-SVM模型的快速公交停站时间组合预测方法[J].东南大学学报(自然科学版),2016,46(3):651-656.
[12]林叶倩,李文权,邱丰,等.可变线路式公交车辆调度优化模型[J].交通信息与安全,2012,30(5):14-18.
[2]高煦明.固定站点需求响应式接驳公交调度模型研究[D].长沙:东南大学(硕士学位论文),2015.
[3]王颖.需求响应型公共交通系统框架设计及适应性评价关键技术研究[D].济南:山东大学(硕士学位论文),2012.
[4]邱丰.可变线路式公交运营调度与模式优化研究[D].长沙:东南大学(博士学位论文),2015.
[5] Alshalalfah B, Shalaby A. Feasibility of Flex-Route as a Feeder Transit Service to Rail Stations in the Suburbs:Case Study in Toronto[J]. Journal of Urban Planning&Development, 2012,138(1):90-100.
[6] Patnaik J, Chien S, Bladikas A. Estimation of Bus Arrival Times Using APC Data[J]. Physical Review Letters, 2010,7(1):128001-128100.
[7]周雪梅,杨晓光,王磊.公交车辆行程时间预测方法研究[J].交通信息与安全,2002,20(6):12-14.
[8]陈坚,李武,吴丹,等.基于MAST的智慧公交系统调度优化模型[J].重庆交大学学报(自然科学版),2016(5):140-145.
[9] Shalaby A, Eng P, Farhan A, et al. Bus travel time prediction model for dynamic operations control and passenger information systems[J]. Steroids, 2002,53(1-2):131-147.
[10]徐荣荣.需求响应型公交在农村客运中的应用研究[D].西安:长安大学(硕士学位论文),2015.
[11]杨敏,丁剑,王炜.基于ARIMA-SVM模型的快速公交停站时间组合预测方法[J].东南大学学报(自然科学版),2016,46(3):651-656.
[12]林叶倩,李文权,邱丰,等.可变线路式公交车辆调度优化模型[J].交通信息与安全,2012,30(5):14-18.