Critical Analysis Presentation: T-Drive: Driving Directions based on Taxi Trajectories

1. Critical Analysis Presentation:T-Drive: Driving Directions based on Taxi Trajectories Authors of Paper: Jing Yuan, Yu Zheng, Chengyang Zhang, WeileiXie, Xing Xie, Guangzhong Sun, Yan Huang Presentation by: YashuChamber, Zhe Jiang CSCI: 8715 Instructor: Dr. Shashi Shekhar

2. Problem Statement • INPUT: Taxi trajectory data using collected using GPS; user query with a start point qs , end point qd, and a departure time td, • OUTPUT: To find a route from qs to qdwith departure time td in a dynamic road network Gr = (Vr, Er) which is learned from a trajectory archive A. • OBJECTIVE: The route should be fastest. • CONSTRAINTS: • dynamic road network Gr = (Vr, Er) in which travel time cost change over time and space • low sample rates of GPS points in trajectory archives

3. Significance To Course • Trajectory data is both spatial and temporal in nature. This work could complement the course content as novel technique in spatial and temporal data mining. • This paper shows an interesting application of spatial and temporal data mining to “smart driving directions” recommendation.

4. Challenges • difficulty in modeling intelligence of taxi drivers based on taxi historical trajectories, • the sparseness and low coverage of trajectory data • low sampling rate in GPS points.

5. Major Contributions • Introduce the notion of “landmark graph” that models the intelligence of taxi drivers based on taxi trajectories. • Proposed a “Variance-Entropy-Based Clustering” method to summarize the distributions of travel times between any two landmarks at any time. • Build a ‘system’ to suggest ‘fastest driving directions’ based on real world dataset, evaluate its performance with both synthetic queries and in-field-experiments.

6. Key Concepts • Trajectory: A sequence of GPS points pertaining to one trip. • Road Segments: A directed edge (one or bi-directional) between two segment terminal points. • Landmarks: ‘k’ frequently traversed road segments. • Landmark Edges: Edge connecting two landmarks if a good number of trajectories (δ) are passing through these two landmarks, and the travel time is less than(tmax). • Rough Routing: Sequence of landmark edges having the fastest route for a given user (based on user’s speed). • Refined Routing: Each Landmark edge in the rough route is transformed into a number of road segments having the fastest route.

7. Approach • Trajectory Preprocessing • Trajectory segmentaion: Segement the GPS log of taxis into individual trips. • Landmark Graph Construction • identify the most ‘k’ frequently traversed road segments, which they denote as “landmarks”. • Different landmarks are connected by an edge (“landmark edge”) if a good number of trajectories (δ) are passing through these two landmarks, and the travel time is less than(tmax). • Route Computing. • Rough Routing: Identify a sequence of landmark edges representing the fastest route. • Refined Routing.

8. Figures Describing The Approach Source: T-Drive: Driving Directions Based on Taxi Trajectories. Author: Jing Yuan

9. Validation Methodology • Evaluating landmark graphs. • RESULT: Used to validate the modeling efficiency of the method. • Evaluation based on synthetic queries. • The authors randomely generate 1200 and compare against Speed-constraint based method (SC) which is used in Google and Bing maps, and the Real-time-traffic-analysis-based method. RESULT: Suggested method outperforms state of the art methods RT and SC.

10. 3. In-the-field-evaluation. • Same driver traverses routes suggested by their method and competetive techniques. • Different drivers (with similar skills) travel different routes suggested by different methods. Strengths and Weaknesses of the methodology. The authors did a good job of evaluating using different methodologies, including real and synthetic evaluations using real dataset.

11. Assumptions • The normal users are allowed on the same roads where taxi trajectories exist. Travel time cost of taxis are same as other type of vehicles. • There is periodicity of travel time cost over week, i.e. accidents, road works, emergenciesand so on that change traffic flow patterns are rare. • The taxi drivers have knowledge of fastest route and do not make “roundabout trip”. • *Historical taxi trajectories cover all potential points.

12. Suggestions • Remove data sparseness and low coverage from challenge section and mention it in a scope paragraph, since it is not conquered. • Provide more justification of deciding travel time cost for a user based on user optimism index • Provide theoretical cost model of suggested method. • Add more in-field evaluations, current sample size (two) is too small.