Efficient Ridesharing Framework for Ride-matching via Heterogeneous Network Embedding

Abstract

Ridesharing has attracted increasing attention in recent years, and combines the flexibility and speed of private cars with the reduced cost of fixed-line systems to benefit alleviating traffic pressure. A major issue in ridesharing is the accurate assignment of passengers to drivers, and how to maximize the number of rides shared between people being assigned to different drivers has become an increasingly popular research topic. There are two major challenges facing ride-matching: scalability and sparsity. Here, we show that network embedding drives the optimal matches between drivers and riders. Contrary to existing approaches that merely depend on the proximity between passengers and drivers, we employ a heterogeneous network to learn the latent semantics from different choices in two types of ridesharing, and extract features in terms of user trajectories and sentiment. A novel framework for ridesharing, RShareForm, which encodes not only the objects but also a variety of semantic relationships between them, is proposed. This article extends the existing skip-gram model to incorporate meta-paths over a proposed heterogeneous network. It allows diverse features to be used to search for similar participants and then ranks them to improve the quality of ride-matching. Extensive experiments on a large-scale dataset from DiDi in Chengdu, China show that by leveraging heterogeneous network embedding with meta paths, RShareForm can significantly improve the accuracy of identifying the participants for ridesharing over existing methods, including both meta-path guided similarity search methods and variants of embedding methods. CCS Concepts: • Human-centered computing → Empirical studies in ubiquitous and mobile computing;

Who Will Travel With Me? Personalized Ranking Using Attributed Network Embedding for Pooling

“…In addition, the riders are often connected to multiple typed objects, such as a special location (e.g., work place), or a fellow rider (e.g., friend) when traveling, which correspond to various kinds of relations. It is thus possible for network-based methods [45] to represent complicated travel features and the connections between them, such as when and where the participants should meet to minimize their trip costs [46].…”

“…The meta-path "ULU" means that we can assign a driver to a passenger if they are connected by a path containing a shared location. Details can be found in our previous work [46], which focuses more on striving for optimal matches via the spatiotemporal relationships between riders and drivers, with no consideration for personal preferences on shared trips.…”

Intelligent Shared Mobility Systems: A Survey on Whole System Design Requirements, Challenges and Future Direction

“…The efficiency of a solution to the assignment challenge is evaluated by how fast it can process a request in a system with millions of other requests and vehicles An efficient approach needs to capture the highly dynamic nature of the distributed ride-sharing environment [34] and improve the accuracy of identifying the participants to better improve the computational efficiency [159]. In [108], the authors presented an efficient symmetry breaking model to reduce the search space by considering the complex switching rider problem.…”

“…To address generating a robust matching system, it is possible to batch requests that arrive within a short time to further optimise the total travel distance [111], [126], demand forecasting in scheduling and re-balancing to improve quality of service [13] and modelling road congestion for better route proposition [34]. Training data with new features could be added to the ride-matching system including money constraints, gender or friendship [64], [118], users' social links, interests, influences, concerns for the level of service quality and travel activity patterns, drivers' willingness [159], drivers' different preferences on how to search for clients and their reasoning [137], and characteristics of tours and trips [160]. The future of work is being shaped by studying the uncertainty in preferences of users [121], training on new data arriving sequentially in online learning [159] and conceptual models to evaluate the interrelationship and causal effects among the parties [162].…”

“…Training data with new features could be added to the ride-matching system including money constraints, gender or friendship [64], [118], users' social links, interests, influences, concerns for the level of service quality and travel activity patterns, drivers' willingness [159], drivers' different preferences on how to search for clients and their reasoning [137], and characteristics of tours and trips [160]. The future of work is being shaped by studying the uncertainty in preferences of users [121], training on new data arriving sequentially in online learning [159] and conceptual models to evaluate the interrelationship and causal effects among the parties [162]. Addressing privacy concerns [12], bid estimation [65], and travel cost distribution policies [162] should be studied to improve the system reliability.…”