ABSTRACT
Public transportation route planning is crucial for both traffic management authority and residents. Current procedures for deciding new routes are time-consuming and ineffective due to the complicate simulation process or overwhelming numbers of opinions from stockholders. In this paper, we propose a novel decision supporting tool, Quantitative and Qualitative Itinerary Planner (QQIP), to help governments pre-evaluate new route services in the city in a timely manner. The function of QQIP is three-fold: visualization of urban informatics, a flexible interface for sketching designate routes, and passenger flows estimation in certain time intervals. With acquired relevant urban information, user can pre-estimate the effectiveness of designed routes using QQIP. To capture the spatial-temporal factors correlated with passenger flows, we propose route-affecting region (RAR) and adopt Deep Neural Network (DNN) framework to combine several dynamic and static features. According to our experimental results on bus-ticket data of Tainan city, the proposed RAR-based feature engineering methods are effective for handling and combining high-correlated dynamic and static data; meanwhile, QQIP can help decision makers infer the passenger flow effectively and efficiently for given designated routes.
- [1] H. Cancela, A. Mauttone and M. E. Urquhart. 2015. Mathematical programming formulations for transit network design. Transportation Research Part B: Methodological 77: 17-37.Google ScholarCross Ref
- [2] S. Cheng, B. Liu and B. Zhai. 2010. Bus arrival time prediction model based on APC data. In the 6th Advanced Forum on Transportation of China. DOI: 10.1049/cp.2010.1123.Google Scholar
- [3] S. I.-J. Chien, Y. Ding, C. Wei. 2002. Dynamic Bus Arrival Time Prediction with Artificial Neural Networks. Journal of Transportation Engineering 128(5): 429-438.Google ScholarCross Ref
- [4] T. M. Cover and J. A. Thomas. 1991. Entropy, relative entropy and mutual information. In Elements of Information Theory, ch. 2, sec. 1, pp. 12-13.Google Scholar
- [5] V. Guihaire and J.-K. Hao. 2010. Transit network timetabling and vehicle assignment for regulating authorities. Computers & Industrial Engineering 59(1).Google Scholar
- [6] Y. Lin, X. Yang, N. Zou and L. Jia. 2013. Real-Time Bus Arrival Time Prediction: Case Study for Jinan, China. Journal of Transportation Engineering, 1133-1140.Google ScholarCross Ref
- [7] A. Mauttone and M. E. Urquhart. 2009. A route set construction algorithm for the transit network design problem. Computers & Operations Research 36: 2440-2449.Google ScholarDigital Library
- [8] M. Pternea, K. Kepaptsoglou and M. G. Karlaftis. 2015. Sustainable urban transit network design. Transportation Research Part A: Policy and Practice 77: 276-291.Google ScholarCross Ref
- [9] L. Quadrifoglio and X. Li. 2009. A methodology to derive the critical demand density for designing and operating feeder transit services. Transportation Research Part B: Methodological 43(10): 922-935.Google ScholarCross Ref
- [10] L. A. Silman, Z. Barzily and U. Passy. 1974. Planning the route system for urban buses. Computers & Operations Research 1(2): 201-211.Google ScholarCross Ref
- [11] L. Steg and R. Gifford. 2005. Sustainable transportation and quality of life. Journal of Transport Geography 13(1): 59-69Google ScholarCross Ref
- [12] H.-M. Su and C.-C. Kuan. 2003. Planning and design guidelines. In Design Manual for Urban Sidewalks, ch. 4, sec,1, pp. 1-4.Google Scholar
- [13] W. Y. Szeto and Y. Wu. 2011. A simultaneous bus route design and frequency setting problem for Tin Shui Wai, Hong Kong. European Journal of Operational Research 209(2): 141-155.Google ScholarCross Ref
- [14] Y. Yan, Q. Meng, S. Wang and X. Guo. 2012. Robust optimization model of schedule design for a fixed bus route. Transportation Research Part C: Emerging Technologies 25: 113-121.Google ScholarCross Ref
- [15] Y. Wei and M.-C. Chen. 2012. Forecasting the short-term metro passenger flow with empirical mode decomposition and neural networks. Transportation Research Part C: Emerging Technologies 21(1): 148-162.Google ScholarCross Ref
- [16] W. Zhang, Z. Shi and Z. Luo. 2008. Prediction of urban passenger transport based-on wavelet SVM with quantum-inspired evolutionary algorithm. In 2008 IEEE International Joint Conference on Neural Networks (IJCNN).Google Scholar
Index Terms
- Customizing Your Own Route with QQIP. A Quantitative and Qualitative Itinerary Planner for New Transportation Routes
Recommendations
A Joint Passenger Flow Inference and Path Recommender System for Deploying New Routes and Stations of Mass Transit Transportation
In this work, a novel decision assistant system for urban transportation, called Route Scheme Assistant (RSA), is proposed to address two crucial issues that few former researches have focused on: route-based passenger flow (PF) inference and multivariant ...
An intelligent and interactive route planning maker for deploying new transportation services
SIGSPATIAL '18: Proceedings of the 26th ACM SIGSPATIAL International Conference on Advances in Geographic Information SystemsIn this work, we propose a novel system, called Route Planning Maker (RPM) to help the government or transportation companies to design new route services in the city. The RPM system has a flexible user interface that allows users design the nearby ...
A Route-Affecting Region Based Approach for Feature Extraction in Transportation Route Planning
Machine Learning and Knowledge Discovery in Databases: Applied Data Science TrackAbstractTraffic deployment is highly correlated with the quality of life. Current research for passenger flow estimation in transportation route planning focuses on origin-destination matrices (OD) analysis; however, we claim that urban functions and ...
Comments