Abstract
Arriving at a destination within a specific time window is important in many transportation settings. For example, trucks may be penalized for early or late arrivals at compact terminals, and early and late arrivals at general practitioners, dentists, and so on, are also discouraged, in part due to COVID. We propose foundations for routing with arrival-window constraints. In a setting where the travel time of a road segment is modeled by a probability distribution, we define two problems where the aim is to find a route from a source to a destination that optimizes or yields a high probability of arriving within a time window while departing as late as possible. In this setting, a core challenge is to enable comparison between paths that may potentially be part of a result path with the goal of determining whether a path is uninteresting and can be disregarded given the existence of another path. We show that existing solutions cannot be applied in this problem setting and instead propose novel comparison methods. Additionally, we introduce the notion of Stochastic Arrival-Window Contraction Hierarchies that enable accelerated query processing in the article’s setting. Next, we present routing algorithms that exploit the above comparison methods in combination with so-called pivot paths and contraction hierarchies to enable efficient processing of the two types of queries. Finally, a detailed experimental study provides empirical insights that justify the need for the two types of routing and also offers insight into key characteristics of the problem solutions.
- [1] . 2018. Terminal appointment booking system (TABS). Retrieved from https://www.ppa.com.ph/sites/default/files/issuances_docs/AO%20006-2018.pdfGoogle Scholar
- [2] . 2016. Managing customer arrivals with time windows: A case of truck arrivals at a congested container terminal. Ann. Operat. Res. 244, 2 (2016), 349–365.Google ScholarCross Ref
- [3] . 2020. Fast stochastic routing under time-varying uncertainty. VLDB J. 29, 4 (2020), 819–839.Google ScholarCross Ref
- [4] . 2009. Shortest path problem considering on-time arrival probability. Transport. Res. Part B: Methodol. 43, 6 (2009), 597–613.
DOI: Google ScholarCross Ref - [5] . 2008. Contraction hierarchies: Faster and simpler hierarchical routing in road networks. In WEA. 319–333. Google Scholar
- [6] . 2013. Minimum time-dependent travel times with contraction hierarchies. J. Exp. Algor. 18 (2013), 1–1.Google Scholar
- [7] . 2013. Vehicle routing problem with stochastic travel times including soft time windows and service costs. Comput. Operat. Res. 40, 1 (2013), 214–224.Google ScholarDigital Library
- [8] . 2012. Robust vehicle routing problem with deadlines and travel time/demand uncertainty. J. Operat. Res. Soc. 63, 9 (2012), 1294–1306.Google ScholarCross Ref
- [9] . 2008. Vehicle routing with soft time windows and Erlang travel times. J. Operat. Res. Soc. 59, 9 (2008), 1220–1228.Google ScholarCross Ref
- [10] . 2017. A multiagent-based approach for vehicle routing by considering both arriving on time and total travel time. ACM Trans. Intell. Syst. Technol. 9, 3 (2017), 1–21.Google ScholarDigital Library
- [11] . 2019. School bus routing and scheduling with stochastic time-dependent travel times considering on-time arrival reliability. Comput. Industr. Eng. 138 (2019), 106125.Google ScholarCross Ref
- [12] . 2018. Finding top-k optimal sequenced routes. In ICDE. 569–580.Google Scholar
- [13] . 2020. The vehicle routing problem with arrival time diversification on a multigraph. Eur. J. Operat. Res. 286, 2 (2020), 564–575.Google ScholarCross Ref
- [14] . 2019. Vehicle routing with arrival time diversification. Eur. J. Operat. Res. 275, 1 (2019), 93–107.Google ScholarCross Ref
- [15] . 2014. Stochastic skyline route planning under time-varying uncertainty. In ICDE. 136–147.Google Scholar
- [16] . 2018. Risk-aware path selection with time-varying, uncertain travel costs: A time series approach. VLDB J. 27, 2 (2018), 179–200.Google ScholarDigital Library
- [17] . 2020. Anytime stochastic routing with hybrid learning. Proc. VLDB Endow. 13, 9 (2020), 1555–1567.Google ScholarDigital Library
- [18] . 2018. Stochastic shortest path finding in path-centric uncertain road networks. In MDM. 40–45.Google Scholar
- [19] . 2017. Enabling time-dependent uncertain eco-weights for road networks. GeoInformatica 21, 1 (2017), 57–88.Google ScholarDigital Library
- [20] . 2020. Stochastic origin-destination matrix forecasting using dual-stage graph convolutional, recurrent neural networks. In ICDE. 1417–1428.Google Scholar
- [21] . 2015. Toward personalized, context-aware routing. VLDB J. 24, 2 (2015), 297–318.Google ScholarDigital Library
- [22] . 2020. Context-aware, preference-based vehicle routing. VLDB J. 29, 5 (2020), 1149–1170.Google ScholarCross Ref
- [23] . 2018. Learning to route with sparse trajectory sets. In ICDE. 1073–1084.Google Scholar
- [24] . 2018. PACE: A PAth-CEntric paradigm for stochastic path finding. VLDB J. 27, 2 (2018), 153–178.Google ScholarDigital Library
- [25] . 2016. Path cost distribution estimation using trajectory data. Proc. VLDB 10, 3 (2016), 85–96.Google ScholarDigital Library
- [26] . 2020. A hybrid learning approach to stochastic routing. In ICDE. 2010–2013.Google Scholar
- [27] . 2022. Towards spatio- temporal aware traffic time series forecasting. In ICDE. 2900–2913.Google Scholar
- [28] . 2021. EnhanceNet: Plugin neural networks for enhancing correlated time series forecasting. In ICDE. 1739–1750.Google Scholar
- [29] . 2022. Triformer: Triangular, variable-specific attentions for long sequence multivariate time series forecasting. In IJCAI, (Ed.). 1994–2001.Google Scholar
- [30] . 2022. AutoCTS: Automated correlated time series forecasting. Proc. VLDB Endow. 15, 4 (2022), 971–983.Google ScholarDigital Library
- [31] . 2023. AutoCTS+: Joint neural architecture and hyperparameter search for correlated time series forecasting. Proc. ACM Manage. Data 1, 1 (2023), 97:1–97:26.Google ScholarDigital Library
- [32] . 2012. Speedup techniques for the stochastic on-time arrival problem. In 12th Workshop on Algorithmic Approaches for Transportation Modelling, Optimization, and Systems. Schloss Dagstuhl-Leibniz-Zentrum fuer Informatik.Google Scholar
- [33] . 2014. Stochastic skyline route planning under time-varying uncertainty. In ICDE. 136–147.Google Scholar
- [34] . 2006. Optimal route planning under uncertainty. In ICAPS, 131–141.Google Scholar
- [35] . 1995. Path planning under time-dependent uncertainty. In UAI. 532–539. Google Scholar
- [36] . 2016. Finding non-dominated paths in uncertain road networks. In ACM SIGSPATIAL. 15:1–15:10.Google Scholar
- [37] . 2012. Route planning for bicycles–exact constrained shortest paths made practical via contraction hierarchy. In ICAPS.Google Scholar
- [38] . 2012. Exact routing in large road networks using contraction hierarchies. Transport. Sci. 46, 3 (2012), 388–404.Google ScholarDigital Library
- [39] . 2019. Real-time traffic assignment using engineered customizable contraction hierarchies. J. Exp. Algor. 24 (2019), 1–28.Google ScholarDigital Library
- [40] . 2021. Unsupervised path representation learning with curriculum negative sampling. In IJCAI. 3286–3292.Google Scholar
- [41] . 2022. Weakly-supervised temporal path representation learning with contrastive curriculum learning. In ICDE. 2873–2885.Google Scholar
Index Terms
- Stochastic Routing with Arrival Windows
Recommendations
Robust Routing Using Electrical Flows
Generating alternative routes in road networks is an application of significant interest for online navigation systems. A high quality set of diverse alternate routes offers two functionalities - (a) support multiple (unknown) preferences that the user ...
Efficient Routing on Large Road Networks Using Hierarchical Communities
Efficient routing is essential in everyday life. Although various hierarchical algorithms exist for computing shortest paths, their heavy precomputation/storage costs and/or query costs hinder their application to large road networks. By detecting a ...
A Hybrid Metaheuristic for Routing in Road Networks
ITSC '15: Proceedings of the 2015 IEEE 18th International Conference on Intelligent Transportation SystemsComputing the optimal route to go from one place to another is a highly important issue in road networks. The problem consists of finding the path that minimizes a metric such as distance, time, cost etc. to go from one node to another in a directed or ...
Comments