Building Efficient Probability Transition Matrix Using Machine Learning from Big Data for Personalized Route Prediction

Ma, Yuan; Di, Junru; Murphey, Yi Li; Qiu, Shiqi; Kristinsson, Johannes; Meyer, Jason; Tseng, Finn; Feldkamp, T.M.

doi:10.1016/j.procs.2015.07.305

Cited by 16 publications

(7 citation statements)

References 8 publications

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“…The method is based on Hidden Markov Models (HMM) trained from the driver's past history. A route prediction algorithm that predicts a driving route for a given pair of origin and destination was presented by Wang et al 2015. Also based on the first order Markov model, the algorithm uses a probability transition matrix that was constructed to represent the knowledge of the driver's preferred links and routes.…”

Section: Related Workmentioning

confidence: 99%

Learn-n-Route: Learning implicit preferences for vehicle routing

Canoy,

Bucarey,

Mandi

et al. 2021

Preprint

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We investigate a learning decision support system for vehicle routing, where the routing engine learns implicit preferences that human planners have when manually creating route plans (or routings). The goal is to use these learned subjective preferences on top of the distance-based objective criterion in vehicle routing systems. This is an alternative to the practice of distinctively formulating a custom VRP for every company with its own routing requirements. Instead, we assume the presence of past vehicle routing solutions over similar sets of customers, and learn to make similar choices. The learning approach is based on the concept of learning a Markov model, which corresponds to a probabilistic transition matrix, rather than a deterministic distance matrix. This nevertheless allows us to use existing arc routing VRP software in creating the actual routings, and to optimize over both distances and preferences at the same time. For the learning, we explore different schemes to construct the probabilistic transition matrix that can co-evolve with changing preferences over time. Our results on a use-case with a small transportation company show that our method is able to generate results that are close to the manually created solutions, without needing to characterize all constraints and sub-objectives explicitly. Even in the case of changes in the customer sets, our method is able to find solutions that are closer to the actual routings than when using only distances, and hence, solutions that require fewer manual changes when transformed into practical routings.

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Section: Related Workmentioning

confidence: 99%

Learn-n-Route: Learning implicit preferences for vehicle routing

Canoy,

Bucarey,

Mandi

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

“…In the field of route and destination prediction, a substantial amount of research has been conducted-testing different types of techniques and systems to improve predictions [12]. These techniques are simplified to two categories, route matching algorithms [3,[13][14][15] and probabilistic modelling systems [2,12,[16][17][18][19]. Froehlich et al [3] introduced an algorithm that matches the current route to a past route by using a similarity score.…”

Section: Related Workmentioning

confidence: 99%

“…The route with the highest similarity score would then be the predicted route. On the other hand, common probabilistic methods include the use of a Markov chain model and its Hidden Markov Model (HMM) variant [12,19]. Markov processes are essentially without memory, as they predict based on a current state.…”

Section: Related Workmentioning

confidence: 99%

Vehicle Destination Prediction Using Bidirectional LSTM with Attention Mechanism

Casabianca

Zhang

Martínez-García

2021

Sensors

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Satellite navigation has become ubiquitous to plan and track travelling. Having access to a vehicle’s position enables the prediction of its destination. This opens the possibility to various benefits, such as early warnings of potential hazards, route diversions to pass traffic congestion, and optimizing fuel consumption for hybrid vehicles. Thus, reliably predicting destinations can bring benefits to the transportation industry. This paper investigates using deep learning methods for predicting a vehicle’s destination based on its journey history. With this aim, Dense Neural Networks (DNNs), Long Short-Term Memory (LSTM) networks, Bidirectional LSTM (BiLSTM), and networks with and without attention mechanisms are tested. Especially, LSTM and BiLSTM models with attention mechanism are commonly used for natural language processing and text-classification-related applications. On the other hand, this paper demonstrates the viability of these techniques in the automotive and associated industrial domain, aimed at generating industrial impact. The results of using satellite navigation data show that the BiLSTM with an attention mechanism exhibits better prediction performance destination, achieving an average accuracy of 96% against the test set (4% higher than the average accuracy of the standard BiLSTM) and consistently outperforming the other models by maintaining robustness and stability during forecasting.

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“…In the same vein, Ye et al (2015) propose a route prediction method based on a hidden Markov model that can accurately predict an entire route early in the trip. Wang et al (2015) also employ a Markov model; their algorithm relies on a probability transition matrix that is developed to represent the knowledge of the driver's preferred links and routes. For the VRP, Canoy and Guns (2019) show the potential of using a Markov model in an optimization framework.…”

Section: Route Predictionmentioning

confidence: 99%

Learn Global and Optimize Local: A Data-Driven Methodology for Last-Mile Routing

Ghosh¹,

Kuiper²,

Mahes³

et al. 2021

Preprint

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In last-mile routing, the optimization is often framed as a Traveling Salesman Problem to minimize travel time and associated cost. However, solutions stemming from this approach do not match the realized paths as drivers deviate due to navigational considerations and preferences. To prescribe routes that incorporate this tacit knowledge, a data-driven model is proposed that aligns well with the hierarchical structure of delivery data wherein each stop belongs to a zone-a geographical area. First, on the global level, a zone sequence is found as a result of a minimization over a cost matrix which balances historical information and distances between zones. Second, within zones, a sequence of stops is determined, such that integrated with the predetermined zone sequence a full solution is obtained.The methodology allows for several enrichments, such as replacing distances by travel times, different cost structures, and intuition while traversing from one zone to another ensuring a seamless connection.These directions are particularly promising as they propel the approach in the top-tier of submissions to the Last-Mile Routing Research Challenge while maintaining the elegant decomposition that ensures a feasible implementation into practice. From a cognitive perspective, the concurrence between prescribed and realized routes reveals that delivery drivers adopt a hierarchical breakdown of the full problem combining intuition and locally optimal decisions when navigating. Furthermore, experimenting with the trade-off between historical information and travel times exposes that drivers rely more on their intuition at the start, whereas at the end, when returning to the station, only travel time is the concern.

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Building Efficient Probability Transition Matrix Using Machine Learning from Big Data for Personalized Route Prediction

Cited by 16 publications

References 8 publications

Learn-n-Route: Learning implicit preferences for vehicle routing

Learn-n-Route: Learning implicit preferences for vehicle routing

Vehicle Destination Prediction Using Bidirectional LSTM with Attention Mechanism

Learn Global and Optimize Local: A Data-Driven Methodology for Last-Mile Routing

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