Efficient and effective trip planning: a data-driven approach
With the increasing pervasiveness of intelligent transportation, a series of innovative services are developed to facilitate people's daily life. This has enabled the advent of a location-based service: trip planning, which helps plan and manage users' itineraries. Existing literatures commonly focus on a small phase, such as making effective trip destinations recommendation during pre-trip phase, or finding the most suitable travel routes during in-trip phase. However, the combination of three important phases (i.e., pre-trip, in-trip and post-trip) is rarely considered to benefit the wellbeings of both the system and end-users. In this thesis, we will stand at the viewpoint of a trip planning service provider by considering the practical requirements. Our overarching aim is to develop a cohesive, efficient and effective trip planning workflow from the perspective of three consecutive phases: pre-trip, in-trip, and post-trip. In particular, the following problems are investigated: (1) pre-trip recommending top-k POIs for users; (2) in-trip dispatching drivers under peak demand; (3) in-trip finding the pick-up location with streaming users; (4) post-trip identifying top-k traffic bottlenecks on road network. After the trip, the system can leverage and analyze the historical trip data to help improve the user experience. Specifically, we aim to identify traffic bottlenecks in a road network, which can easily lead to traffic congestion by traffic flow propagation and then bring unsatisfying trip experience. These traffic bottlenecks can be used as signals to remind drivers to adjust the current trip dynamically, or help road expansion in physical infrastructure. In this thesis, we deal with the traffic bottleneck identification problem efficiently and effectively by considering the influence among edges. We compute the spread influence of each edge, and then select a set of seed edges with the largest influence.
- Record URL:
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Supplemental Notes:
- PhD thesis
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Authors:
- Luo, H
- Publication Date: 2021
Language
- English
Media Info
- Pagination: 1 file
Subject/Index Terms
- TRT Terms: Advanced traveler information systems; Advanced traveler information systems; Bottlenecks; Demand responsive transportation; Incident detection; Planning; Traffic flow; Travel; Traveler information and communication systems; Trip purpose; Types of roads by network
- Uncontrolled Terms: Road networks
- ATRI Terms: Advanced traveler information systems (ATIS); Bottleneck; Demand responsive transport; Incident detection; Journey purpose; Planning; Road network; Traffic flow
- ITRD Terms: 632: Congestion (traffic); 1157: Demand responsive transport
- Subject Areas: Planning and Forecasting; I72: Traffic and Transport Planning;
Filing Info
- Accession Number: 01850884
- Record Type: Publication
- Source Agency: ARRB Group Limited
- Files: ITRD, ATRI
- Created Date: Jun 30 2022 12:04PM