Modeling Taxi Drivers' Behaviour for the Next Destination Prediction

In this paper, we study how to model taxi drivers' behaviour and geographical information for an interesting and challenging task: the next destination prediction in a taxi journey. Predicting the next location is a well studied problem in human mobility, which finds several applications in real-world scenarios, from optimizing the efficiency of electronic dispatching systems to predicting and reducing the traffic jam. This task is normally modeled as a multiclass classification problem, where the goal is to select, among a set of already known locations, the next taxi destination. We present a Recurrent Neural Network (RNN) approach that models the taxi drivers' behaviour and encodes the semantics of visited locations by using geographical information from Location-Based Social Networks (LBSNs). In particular, RNNs are trained to predict the exact coordinates of the next destination, overcoming the problem of producing, in output, a limited set of locations, seen during the training phase. The proposed approach was tested on the ECML/PKDD Discovery Challenge 2015 dataset - based on the city of Porto -, obtaining better results with respect to the competition winner, whilst using less information, and on Manhattan and San Francisco datasets.Comment: preprint version of a paper submitted to IEEE Transactions on Intelligent Transportation System

Big data analytics:Computational intelligence techniques and application areas

Big Data has significant impact in developing functional smart cities and supporting modern societies. In this paper, we investigate the importance of Big Data in modern life and economy, and discuss challenges arising from Big Data utilization. Different computational intelligence techniques have been considered as tools for Big Data analytics. We also explore the powerful combination of Big Data and Computational Intelligence (CI) and identify a number of areas, where novel applications in real world smart city problems can be developed by utilizing these powerful tools and techniques. We present a case study for intelligent transportation in the context of a smart city, and a novel data modelling methodology based on a biologically inspired universal generative modelling approach called Hierarchical Spatial-Temporal State Machine (HSTSM). We further discuss various implications of policy, protection, valuation and commercialization related to Big Data, its applications and deployment

승차 공유 서비스를 위한 강화학습 기반의 적응형 매칭 시간 간격 결정

학위논문(석사) -- 서울대학교대학원 : 공과대학 건설환경공학부, 2023. 2. 김동규.Ride-hailing services helped daily travel by efficiently matching passengers and drivers. These services face inefficiency in system operations due to supply and demand imbalances. A widely adopted strategy is fixed batch-based matching, which accumulates requests and idle drivers and matches them in batches. Recent studies have proposed adaptive matching time intervals to consider dynamic supply and demand patterns. However, matching failure factors such as passenger request cancellation and driver acceptance are not considered. This study aims to control adaptive matching time intervals based on reinforcement learning considering matching failure factors. To this end, we propose a two-step framework to maximize the matching success rate. First, an agent based on Deep Q-Network (DQN) determines the matching time interval, and then combinatorial optimization is performed based on the driver's acceptance probability. We conduct experiments on various supply-demand patterns based on synthetic and real datasets and compare performance with previous strategies. We confirmed that the proposed strategy reduces the proportion of expired requests and achieves the highest matching success rate. We also discussed the trade-off between fixed matching time intervals and matching success rates and interpreted agent policies. Our approach provides insight by discussing matching failure factors, which cannot be captured with performance alone.승차 공유 서비스들은 승객과 운전자들을 효율적으로 연결함으로써 일상 생활의 이동에 많은 도움을 주고 있다. 이러한 서비스들은 수요와 공급의 불균형 문제로 인해 시스템 운영 측면에서 비효율적인 상황에 직면한다. 이를 위해 일정한 매칭 시간 간격 동안 승객의 요청과 공차 통행 중인 운전자들을 모아 일괄적으로 매칭하는 전략을 주로 사용한다. 최근에는 수요와 공급의 동적 패턴을 효과적으로 반영하기 위한 적응형 매칭 시간 간격에 대한 연구들이 있었으나, 승객의 요청 취소와 운전자 거부와 같은 매칭 실패 요인들은 간과되었다. 본 연구의 목표는 매칭 실패 요인이 존재하는 상황에서 강화학습 기반의 적응형 매칭 시간 간격을 통해 매칭 성공률을 최대화하는 것이다. 연구 방법은 2단계 프레임워크로 구성된다. 먼저 DQN (Deep Q-Network) 기반의 강화학습 에이전트는 각 매칭 시간 간격마다 배차 행동(Dispatch action)을 결정하며, 이후에는 운전자의 수락확률을 기반으로 한 조합최적화가 수행된다. 실제 데이터셋을 기반으로 한 실험을 통해 이전 전략들과 성능을 비교하고 매칭 실패 요인들에 대한 분석을 수행한다. 실험 결과, 제안된 방법은 대부분의 실험에서 가장 높은 매칭 성공률을 보였다. 구체적으로는 운전자의 미 수락에 의한 만료 요청의 비율을 감소시키며, 승객의 요청 취소 비율을 효율적으로 제어하는 것을 확인했다. 또한 학습된 에이전트의 정책 해석과 집계된 결과의 세분화를 기반으로 추가 분석이 수행되었다. 이러한 접근 방식은 매칭 성공률과 세부적인 매칭 실패 요인들에 대한 논의를 통해 기존 연구에서 간과되었던 통찰력을 제공한다.Chapter 1. Introduction 1 Chapter 2. Literature Review 7 Chapter 3. Methodology 11 3.1. Problem Statement 11 3.2. MDP formulation 13 3.3. Simulation Framework 15 3.4. Deep Q-Networks (DQN) 21 Chapter 4. Results 23 4.1. Data Description 23 4.2. Experimental Setup 26 4.3. Experiments on synthetic datasets 29 4.4. Experiments on real datasets 33 Chapter 5. Conclusion 41 Bibliography 44 Abstract in Korean 50석

Better branch prediction through prophet/critic hybrids

The prophet/critic hybrid conditional branch predictor has two component predictors. The prophet uses a branch's history to predict its direction. We call this prediction and the ones for branches following it the branch future. The critic uses the branch's history and future to critique the prophet's prediction. The hybrid combines the prophet's prediction with the critique, either agrees or disagree, forming the branch's overall prediction. Results shows these hybrids can reduce mispredicts by 39 percent and improve processor performance by 7.8 percent.Peer ReviewedPostprint (published version

Knowledge Discovery and Data Mining for Shared Mobility and Connected and Automated Vehicle Applications

The rapid development of shared mobility and connected and automated vehicles (CAVs) has not only brought new intelligent transportation system (ITS) challenges with the new types of mobility, but also brought a huge opportunity to accelerate the connectivity and informatization of transportation systems, particularly when we consider all the new forms of data that is becoming available. The primary challenge is how to take advantage of the enormous amount of data to discover knowledge, build effective models, and develop impactful applications. With the theoretical and experimental progress being made over the last two decades, data mining and machine learning technologies have become key approaches for parsing data, understanding information, and making informed decisions, especially as the rise of deep learning algorithms bringing new levels of performance to the analysis of large datasets. The combination of data mining and ITS can greatly benefit research and advances in shared mobility and CAVs.This dissertation focuses on knowledge discovery and data mining for shared mobility and CAV applications. When considering big data associated with shared mobility operations and CAV research, data mining techniques can be customized with transportation knowledge to initially parse the data. Then machine learning methods can be used to model the parsed data to elicit hidden knowledge. Finally, the discovered knowledge and extracted information can help in the development of effective shared mobility and CAV applications to achieve the goals of a safer, faster, and more eco-friendly transportation systems.In this dissertation, there are four main sections that are addressed. First, new methodologies are introduced for extracting lane-level road features from rough crowdsourced GPS trajectories via data mining, which is subsequently used as the fundamental information for CAV applications. The proposed method results in decimeter level accuracy, which satisfies the positioning needs for many macroscopic and microscopic shared mobility and CAV applications. Second, macroscopic ride-hailing service big data has been analyzed for demand prediction, vehicle operation, and system efficiency monitoring. The proposed deep learning algorithms increase the ride-hailing demand prediction accuracy to 80% and can help the fleet dispatching system reduce 30% of vacant travel distance. Third, microscopic automated vehicle perception data has been analyzed for a real-time computer vision system that can be used for lane change behavior detection. The proposed deep learning design combines the residual neural network image input with time serious control data and reaches 95% of lane change behavior prediction accuracy. Last but not least, new ride sharing and CAV applications have been simulated in a behavior modeling framework to analyze the impact of mobility and energy consumption, which addresses key barriers by quantifying the transportation system-wide mobility, energy and behavior impacts from new mobility technologies using real-world data

Context-aware mobility analytics and trip planning

The study of user mobility is to understand and analyse the movement of individuals in the spatial and temporal domains. Mobility analytics and trip planning are two vital components of user mobility that facilitate the end users with easy to access navigational support through the urban spaces and beyond. Mobility context describes the situational factors that can influence user mobility decisions. The context-awareness in mobility analytics and trip planning enables a wide range of end users to make effective mobility decisions. With the ubiquity of urban sensing technologies, various situational factors related to user mobility decisions can now be collected at low cost and effort. This huge volume of data collected from heterogeneous data sources can facilitate context-aware mobility analytics and trip planning through intelligent analysis of mobility contexts, mobility context prediction, mobility context representation and integration considering different user perspectives. In each chapter of this thesis such issues are addressed through the development of case-specific solutions and real-world deployments. Mobility analytics include prediction and analysis of many diverse mobility contexts. In this thesis, we present several real-world user mobility scenarios to conduct intelligent contextual analysis leveraging existing statistical methods. The factors related to user mobility decisions are collected and fused from various publicly available open datasets. We also provide future prediction of important mobility contexts which can be utilized for mobility decision making. The performance of context prediction tasks can be affected by the imbalance in context distribution. Another aspect of context prediction is that the knowledge from domain experts can enhance the prediction performance however, it is very difficult to infer and incorporate into mobility analytics applications. We present a number of data-driven solutions aiming to address the imbalanced context distribution and domain knowledge incorporation problems for mobility context prediction. Given an imbalanced dataset, we design and implement a framework for context prediction leveraging existing data mining and sampling techniques. Furthermore, we propose a technique for incorporating domain knowledge in feature weight computation to enhance the task of mobility context prediction. In this thesis, we address key issues related to trip planning. Mobility context inference is a challenging problem in many real-world trip planning scenarios. We introduce a framework that can fuse contextual information captured from heterogeneous data sources to infer mobility contexts. In this work, we utilize public datasets to infer mobility contexts and compute trip plans. We propose graph based context representation and query based adaptation techniques on top of the existing methods to facilitate trip planning tasks. The effectiveness of trip plans relies on the efficient integration of mobility contexts considering different user perspectives. Given a contextual graph, we introduce a framework that can handle multiple user perspectives concurrently to compute and recommend trip plans to the end user. This thesis contains efficient techniques that can be employed in the area of urban mobility especially, context-aware mobility analytics and trip planning. This research is built on top of the existing predictive analytics and trip planning techniques to solve problems of contextual analysis, prediction, context representation and integration in trip planning for real-world scenarios. The contributions of this research enable data-driven decision support for traveling smarter through urban spaces and beyond