In recent years, online ride-hailing services have emerged as an important component of urban transportation system, which not only provide significant ease for residents’ travel activities, but also shape new travel behavior and diversify urban mobility patterns. This study provides a thorough review of machine-learning-based methodologies for on-demand ride-hailing services. The importance of on-demand ride-hailing services in the spatio-temporal dynamics of urban traffic is first highlighted, with machine-learning-based macro-level ride-hailing research demonstrating its value in guiding the design, planning, operation, and control of urban intelligent transportation systems. Then, the research on travel behavior from the perspective of individual mobility patterns, including carpooling behavior and modal choice behavior, is summarized. In addition, existing studies on order matching and vehicle dispatching strategies, which are among the most important components of on-line ride-hailing systems, are collected and summarized. Finally, some of the critical challenges and opportunities in ride-hailing services are discussed. 

/pdf/how-machine-learning-informs-ride-hailing-services-a-survey-3rn9dd7u.pdf

How machine learning informs ride-hailing services: A survey

A survey of mobile crowdsensing and crowdsourcing strategies for smart mobile device users

Predicting how long a trip will take may allow travelers plan ahead, save money, and avoid traffic congestion. The journey time estimation model should take into account three crucial factors: (1) individual travel preference, (2) dynamic spatio-temporal correlations, and (3) the association between long-term speed forecast and travel time estimate. In order to overcome these challenges, this study proposes a unique parallel architecture called the multi-task spatio-temporal attention network (MT-STAN) to estimate journey times. To extract the dynamic spatio-temporal correlations of the road network, we first develop a traffic speed prediction model based on spatio-temporal block and bridge transformer networks, combining the road, timestamp, and traffic speed information into hidden states. Second, we offer a personalized model for estimating journey times that makes use of cross-network, holistic attention, and semantic transformer. In this approach, travel preferences extraction through cross-network, holistic attention permits correlations between the dynamic road network’s hidden states and individual journey characteristics, which are subsequently transformed into global semantics by the semantic transformer; preferences and semantics are integrated during the estimate phase. Finally, a multi-task learning component is included, which combines both traffic speed prediction and individual journey time estimate, via the sharing of underlying network parameters and the improvement of the contextual semantic knowledge of the latter job. Evaluation experiments are carried out using a highway dataset collected in Yinchuan City, Ningxia Province, China. The proposed prediction model outperforms state-of-the-art baseline approaches in experiments. 

When Will We Arrive? A Novel Multi-Task Spatio-Temporal Attention Network Based on Individual Preference for Estimating Travel Time

Given an origin (O), a destination (D), and a departure time (T), an Origin-Destination (OD) travel time oracle~(ODT-Oracle) returns an estimate of the time it takes to travel from O to D when departing at T. ODT-Oracles serve important purposes in map-based services. To enable the construction of such oracles, we provide a travel-time estimation (TTE) solution that leverages historical trajectories to estimate time-varying travel times for OD pairs. The problem is complicated by the fact that multiple historical trajectories with different travel times may connect an OD pair, while trajectories may vary from one another. To solve the problem, it is crucial to remove outlier trajectories when doing travel time estimation for future queries. We propose a novel, two-stage framework called Diffusion-based Origin-destination Travel Time Estimation (DOT), that solves the problem. First, DOT employs a conditioned Pixelated Trajectories (PiT) denoiser that enables building a diffusion-based PiT inference process by learning correlations between OD pairs and historical trajectories. Specifically, given an OD pair and a departure time, we aim to infer a PiT. Next, DOT encompasses a Masked Vision Transformer~(MViT) that effectively and efficiently estimates a travel time based on the inferred PiT. We report on extensive experiments on two real-world datasets that offer evidence that DOT is capable of outperforming baseline methods in terms of accuracy, scalability, and explainability.

pdf/origin-destination-travel-time-oracle-for-map-based-services-19eh1763.pdf

Origin-Destination Travel Time Oracle for Map-based Services

A decentralized trust inference approach with intelligence to improve data collection quality for mobile crowd sensing

Estimating the origin-destination travel time is a fundamental problem in many location-based services for vehicles, e.g., ride-hailing, vehicle dispatching, and route planning. Recent work has made significant progress to accuracy, but they largely rely on GPS trajectories which are too coarse to model many personalized driving behaviors, e.g., differentiating novice and veteran drivers. In this paper, we propose Customized Travel Time Estimation (CTTE) that fuses GPS trajectories, smartphone inertial data, and road network within a deep recurrent neural network. It constructs a road link traffic database with topology representation, speed statistics, and query distribution. It also calibrates inertial readings, estimates the arbitrary phone’s pose in car, and detects multiple aggressive driving events (e.g., bump judders, sharp turns, sharp slopes, frequent lane shifts, overspeeds, and sudden brakes). Finally, we demonstrate our solution on two typical transportation problems, i.e., predicting traffic speed at holistic level and estimating customized travel time at personal level, within a multi-task learning structure. Experiments on two large-scale real-world traffic datasets from DiDi platform show our effectiveness compared with the state-of-the-art.

CTTE: Customized Travel Time Estimation via Mobile Crowdsensing

With the wide application of vehicular location-based services, precise estimation of the travel time plays a crucial role in intelligent transportation systems, such as driving navigation, traffic monitoring and route planning. Recent methods have made significant progress on public datasets, but are not satisfied for current ride-hailing platforms with complex road network topology and dynamic traffic fluctuation. In this paper, we propose an end-to-end Deep Fusion framework for Travel Time Estimation (DFTTE), which exploits multi-source heterogeneous traffic information within an Encoder-Decoder architecture. Specifically, we explore a relational fusion network to learn the relationship of road link segments, and employ an attention mechanism to capture efficient correlations among spatial and temporal features. Extensive experiments have been conducted on two large-scale real-world traffic datasets collected by DiDi platform, and the results have demonstrated our effectiveness compared with the state-of-the-art.

Deep Fusion for Travel Time Estimation Based on Road Network Topology

Precise prediction of Multivariate Time Series (MTS) has been playing a pivotal role in numerous kinds of applications. Existing works have made significant efforts to capture temporal tendency and periodical patterns, but they always ignore abrupt variations and heterogeneous/spatial associations of sensory data. In this paper, we develop a dual normalization (dual-norm) based dynamic graph diffusion network (DNGDN) to capture hidden intricate correlations of MTS data for temporal prediction. Specifically, we design time series decomposition and dual-norm mechanism to learn the latent dependencies and alleviate the adverse effect of abnormal MTS data. Furthermore, a dynamic graph diffusion network is adopted for adaptively exploring the spatial correlations among variables. Extensive experiments are performed on 3 real world experimental datasets with 8 representative baselines for temporal prediction. The performances of DNGDN outperforms all baselines with at least 4% lower MAPE over all datasets. 

Dual-norm based dynamic graph diffusion network for temporal prediction

Estimating the traffic incident duration is of great importance to traffic control, traffic navigation, and transportation safety. However, the complex road network topology and dynamic traffic conditions make it challenging. In this paper, we propose a context-aware spatio-temporal graph convolution framework, named TimeBird, to estimate the duration time of traffic incidents. Specifically, we build the dynamic weighted adjacency matrix and traffic incident risk similarity matrix to learn the hidden spatial context correlations based on graph convolution network. Then we employ the historical traffic speed of road segments to learn the temporal dependency. Lastly, we design a context-aware attention mechanism to adaptively learn the heterogeneous traffic features for incident duration prediction. Extensive experiments on two large-scale real-world datasets from DiDi ride-hailing platform demonstrate the effectiveness of TimeBird. 

TimeBird: Context-Aware Graph Convolution Network for Traffic Incident Duration Prediction

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CTTE: Customized Travel Time Estimation via Mobile Crowdsensing

Deep Fusion for Travel Time Estimation Based on Road Network Topology

Dual-norm based dynamic graph diffusion network for temporal prediction

TimeBird: Context-Aware Graph Convolution Network for Traffic Incident Duration Prediction