Estimation of Waste Collection Vehicles' Travel Time Using Spatial-Temporal Artificial Neural Networks Algorithms (Case Study: Tehran City)

Document Type : Research article

Authors

1 student

2 university of tehran

3 teacher

4 5- Assistant Professor, Department of Industrial and Systems Engineering, North Carolina State University

Abstract

Travel time data is important for most of the vehicle routing problems (VRP) aimed at modeling time constraints like customer time window as well as time objectives, such as minimizing travel time and waiting time. One of the most important advantages of precise travel times in VRPs is the reduction of total travel time and increasing route reliability, which with more accurate travel time data better removes time constraints. The obvious reasons for not using variable travel time in VRP are related to three factors. First, conventional VRP algorithms cannot consider variable travel time without major structural changes. Second, all parameters affecting travel time are often not used. Third, collecting the required data is very difficult. Therefore, designing appropriate models with enough accuracy in predicting travel time based on the effective variables is a necessity in the transportation planning process.
In this paper, the appropriate variables for measuring the impact of spatial-temporal and traffic factors on the travel time of medical waste collection vehicles in a single route were defined and by examining and implementing the proposed model, the impact of these variables on the travel time was measured. To estimate the travel time, we used the proposed model called spatial-temporal neural network (ST-NN) which can examine the effect of variables at any moment of the travel time. The study area is Tehran metropolis with its entire urban passages network. The required data were first pre-processed and then entered into the neural network algorithm as model inputs. The model was

Keywords

References

خیراللهی، مصطفی، نادی، سعید و نجمه نیسانی سامانی (1395). «تلفیق معیارهای کیفی و کمی با استفاده از مدل‌های مکان مبنا به‌منظور مسیریابی بهینۀ خودروهای اورژانس در محیط‌های شهری»، مجلۀ سپهر، شمارۀ 100، صص 45-59.
ناصری، محمدرضا و وحید برادران (1388). «بررسی عوامل موثر بر زمان سفر در سیستم حمل و نقل عمومی و پیش بینی زمان سفر مورد کاوی: سیستم اتوبوسرانی شهر تهران». پژوهشنامه حمل و نقل، شمارۀ 3، صص 219-232.
باقری، میلاد، جلوخانی نیارکی، محمدرضا و کیوان باقری (1396). «بررسی پتانسیل اراضی استان کرمانشاه جهت کشت گندم دیم با استفاده از شبکۀ عصبی مصنوعی»، سنجش از دور و سامانۀ اطلاعات جغرافیایی در منابع طبیعی، شمارۀ 4، صص 36-48.
Naseri, M. R., & Baradaran, V. (2009). Study of Factors Affecting Travel Time in Public Transport System and Predicting Travel Time Case Study: Tehran Bus System. Journal of Transportation, No. 3, pp. 219-232. (In Persian)
Bagheri, M., Jaloukhani Niaraki, M. R., & Bagheri, K. (2017). Assessing the Potential of Lands in Kermanshah Province for Rainfed Wheat Cultivation Using Artificial Neural Network. Remote Sensing and Geographic Information System in Natural Resources, (4), 36- 48. (In Persian)
Brakewood, C., Macfarlane, G. S., & Watkins, K. (2015). The Impact of Real-Time Information on Bus Ridership in New York City. Transportation Research Part C: Emerging Technologies, 53, 59-75.
Cheng, J., Li, G., & Chen, X. (2019). Developing a Travel Time Estimation Method of Freeway Based on Floating Car Using Random Forests. Journal of Advanced Transportation, 63, 51-65.
Cheng, Z., Chow, M. Y., Jung, D., & Jeon, J. (2017). A Big Data Based Deep Learning Approach for Vehicle Speed Prediction. In 26th International Symposium on Industrial Electronics (ISIE) (pp. 389-394). IEEE.
Dessouky, M., Hall, R., Zhang, L., & Singh, A. (2003). Real-Time Control of Buses for Schedule Coordination at a Terminal. Transportation Research Part A: Policy and Practice, 37(2), 145-164.
Goldberg, R., & Listowsky, P. (1994). Critical Factors for Emergency Vehicle Routing Expert Systems. Expert Systems with Applications, 7(4), 589-602.
He, J., Shen, W., Divakaruni, P., Wynter, L., & Lawrence, R. (2013, June). Improving traffic prediction with tweet semantics. In Twenty-Third International Joint Conference on Artificial Intelligence.
Hellinga, B., Izadpanah, P., Takada, H., & Fu, L. (2008). Decomposing Travel Times Measured by Probe-Based Traffic Monitoring Systems to Individual Road Segments. Transportation Research Part C: Emerging Technologies, 16(6), 768-782.
Hofleitner, A., Herring, R., & Bayen, A. (2012). Arterial Travel Time Forecast with Streaming Data: A Hybrid Approach of Flow Modeling and Machine Learning. Transportation Research Part B: Methodological, 46(9), 1097-1122.
Khairullahi, M., Nadi, S., & Nissani Samani, N. (2016). Integration of Qualitative and Quantitative Criteria Using Base Location Models in Order to Optimally Route Emergency Vehicles in Urban Environments. Sepehr Magazine, 25(100), 45-59. (In Persian)
Kiartzis, S. K., Bakirtzis, A. G., & Petridis, V. (1992). Short-Term Load Forecasting Using Neural Networks. Electric Power Systems Research, 33, 1-6.
Kumar, B. A., Vanajakshi, L., & Subramanian, S. C. (2017). Bus Travel Time Prediction Using a Time-Space Discretization Approach. Transportation Research Part C: Emerging Technologies, 79, 308-332.
Kumar, B. A., Vanajakshi, L., & Subramanian, S. C. (2018). A Hybrid Model Based Method for Bus Travel Time Estimation. Journal of Intelligent Transportation Systems, 22(5), 390-406.
Li, Y., & McDonald, M. (2002). Link Travel Time Estimation Using Single GPS Equipped Probe Vehicle. In Proceedings the IEEE 5th International Conference on Intelligent Transportation Systems (pp. 932-937). IEEE
Lin, H. E., Zito, R., & Taylor, M. (2005, September). A review of travel-time prediction in transport and logistics. In Proceedings of the Eastern Asia Society for transportation studies (Vol. 5, pp. 1433-1448).
Liu, H., Xu, H., Yan, Y., Cai, Z., Sun, T., & Li, W. (2020). Bus Arrival Time Prediction Based on LSTM and Spatial-Temporal Feature Vector. IEEE Access, 8, 11917-11929.
Lu, L., Wang, J., He, Z., & Chan, C. Y. (2017). Real-time estimation of freeway travel time with recurrent congestion based on sparse detector data. IET Intelligent Transport Systems, 12(1), 2-11.
Ma, Z., Koutsopoulos, H. N., Ferreira, L., & Mesbah, M. (2017). Estimation of Trip Travel Time Distribution Using a Generalized Markov Chain Approach. Transportation Research Part C: Emerging Technologies, 74, 1-21.
Pahlavani, P., Delavar, M. R., & Frank, A. U. (2012). Using a Modified Invasive Weed Optimization Algorithm for a Personalized Urban Multi-Criteria Path Optimization Problem. International Journal of Applied Earth Observation and Geoinformation, 18, 313-328.
Rahman, M. M., Wirasinghe, S. C., & Kattan, L. (2018). Analysis of Bus Travel Time Distributions for Varying Horizons and Real-Time Applications. Transportation Research Part C: Emerging Technologies, 86, 453-466.
Rahmani, M., Jenelius, E., & Koutsopoulos, H. N. (2015). Non-Parametric Estimation of Route Travel Time Distributions from Low-Frequency Floating Car Data. Transportation Research Part C: Emerging Technologies, 58, 343-362.
Watkins, K. E., Ferris, B., Borning, A., Rutherford, G. S., & Layton, D. (2011). Where Is My Bus? Impact of Mobile Real-Time Information on the Perceived and Actual Wait Time of Transit Riders. Transportation Research Part A: Policy and Practice, 45(8), 839-848.
Woodard, D., Nogin, G., Koch, P., Racz, D., Goldszmidt, M., & Horvitz, E. (2017). Predicting Travel Time Reliability Using Mobile Phone GPS Data. Transportation Research Part C: Emerging Technologies, 75, 30-44.
Yang, M., Chen, C., Wang, L., Yan, X., & Zhou, L. (2016). Bus Arrival Time Prediction Using Support Vector Machine with Genetic Algorithm. Neural Network World, 26(3), 205-217.
Zakaria, M., Al-Shebany, M., & Sarhan, S. (2014). Artificial neural network: a brief overview. International Journal of Engineering Research and Applications, 4(2), 7-12.
Zheng, F., & Van Zuylen, H. (2013). Urban Link Travel Time Estimation Based on Sparse Probe Vehicle Data. Transportation Research Part C: Emerging Technologies, 31, 145-157.
Zhou, M., Wang, D., Li, Q., Yue, Y., Tu, W., & Cao, R. (2017). Impacts of Weather on Public Transport Ridership: Results from Mining Data from Different Sources. Transportation research part C: Emerging Technologies, 75, 17-29.
Geographical Urban Planning Research (GUPR)
Volume 9, Issue 1
April 2021
Pages 75-92

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