Analyzing Travel Patterns for Scheduling in a Dynamic Environment
02 Sep 2013-pp 304-318
TL;DR: This work extends the work of previous authors in this domain by incorporating some real life constraints (varying travel patterns, flexible meeting point and considering road network distance) and generalizes the problem by considering variable number of users.
Abstract: Scheduling a meeting is a difficult task for people who have overbooked calendars and many constraints. This activity becomes further complex when the meeting is to be scheduled between parties who are situated in geographically distant locations of a city and have varying traveling patterns. We extend the work of previous authors in this domain by incorporating some real life constraints (varying travel patterns, flexible meeting point and considering road network distance). We also generalize the problem by considering variable number of users. The previous work does not consider these dimensions. The search space for optimal meeting point is reduced by considering convex hull of the set of users locations. It can be further pruned by considering other factors, e.g., direction of movement of users. Experiments are performed on a real-world dataset and show that our method is effective in stated conditions.
Citations
More filters
TL;DR: This work proposes a novel approach to forecast the future traffic speed of the road segments (links) based on traffic flow data without the need for previous traffic speed as input, and uses a convolutional attention-based recurrent neural network to do this.
Abstract: Traffic speed forecasting becomes a thriving research area in modern transportation systems. The intensification of travel flow volumes due to fast urbanization, vehicle path planning, demands on efficient transport planning policies, commercial objectives, and many other factors contribute to fuel this revival dynamics. Moreover, predicting vehicle speed is of paramount importance in congestion management to help transport authorities as well as network users to handle congestion over road infrastructures or to provide a global overview of daily passenger flow. In this work, we propose a novel approach to forecast the future traffic speed of the road segments (links) based on traffic flow data without the need for previous traffic speed as input. To do this, we first pre-process floating car data of several million vehicles for multiples network links spread all over the Greater Paris area from 2016 to 2017. A convolutional attention-based recurrent neural network is used to capture the local-temporal features of traffic data to unveil the underlying pattern between the traffic flow and speed sequences for all links over the network. While the convolutional layer captures the local dependency, the attention layer learns patterns from weights of near-term traffic flow. It extracts the inherent interdependency of traffic speed due to many factors such as incidents, rush hour, land use, to cite a few, in non-free-flow conditions. The efficiency of the proposed model is evaluated using several metrics in traffic speed forecasting excluding additional data such as historical traffic speed and network graph contrary to cutting-edge work in the field. This is a substantial property since it allows avoiding the cumbersomeness in data mixing and facilitating resource availability. The proposed model is also evaluated on several roads located in the Greater Paris area separately on weekdays and weekends.
23 citations
TL;DR: A STS data model is proposed which captures both non-spatial and spatial properties of moving users, connected on social network and extends spatiotemporal data model for moving objects proposed in Ferreira et al. (Trans GIS 18(2):253–269, 2014).
Abstract: A location-based social network is a network representation of social relations among actors, which not only allow them to connect to other users/friends but also they can share and access their physical locations. Here, the physical location consists of the instant location of an individual at a given timestamp and the location history that an individual has accumulated in a certain period. This paper aimed to capture this spatiotemporal social network (STS) data of location-based social networks and model it. In this paper, we propose a STS data model which captures both non-spatial and spatial properties of moving users, connected on social network. In our model, we define data types and operations that make querying spatiotemporal social network data easy and efficient. We extend spatiotemporal data model for moving objects proposed in Ferreira et al. (Trans GIS 18(2):253–269, 2014) for social networks. The data model infers individual’s location history and helps in querying social network users for their spatiotemporal locations, social links, influences, their common interests, behavior, activities, etc. We show the some results of applying our data model on a spatiotemporal dataset (GeoLife) and two large real-life spatiotemporal social network datasets (Gowalla, Brightkite) collected over a period of two years. We apply the proposed model to determine interesting locations in the city and correlate the impact of social network relationships on the spatiotemporal behavior of the users.
6 citations
20 Oct 2020
TL;DR: This work proposes a novel model, called the Local and Global Spatial Temporal Network (LGSTN), to forecast the traffic flows on a road segment basis (instead of regions), and considerably outperforms state-of-the-art traffic forecast methods.
Abstract: Traffic flow forecasting is significant to traffic management and public safety. However, it is a challenging problem, because of complex spatial and temporal dependencies. Many existing approaches adopt Graph Convolution Networks (GCN) to model spatial dependencies and recurrent neural networks (RNN) to model temporal dependencies, simultaneously. However, the existing approaches mainly use adjacency matrix or distance matrix to represent the correlations between adjacent road segments, which fail to capture dynamic spatial dependencies. Besides, these approaches ignore the lag influence caused by propagation times of traffic flows and cannot model the global aggregation effect of traffic flows. In response to the limitations of the existing approaches, we model local aggregation and global aggregation of traffic flows. We propose a novel model, called the Local and Global Spatial Temporal Network (LGSTN), to forecast the traffic flows on a road segment basis (instead of regions). We first construct time-dependent flow transfer graphs to capture dynamic spatial correlations among the local traffic flows of the adjacent road segments. Next, we adopt spatial-based GCNs to model local traffic flow aggregation. Then, we propose a Lag-gated LSTM to model global traffic flow aggregation by considering free-flow reachable time matrix. Experiments on two real-world datasets have demonstrated our proposed LGSTN considerably outperforms state-of-the-art traffic forecast methods.
1 citations
TL;DR: This work proposes a solution to determine optimal meeting location for two moving users in the Euclidean space and generalizes the problem by considering variable number of moving users and evaluating optimal meeting point on the road network.
Abstract: Scheduling a meeting is a difficult task for people who have overbooked calendars and often have many constraints. This activity becomes further complex when the meeting is to be scheduled between parties who are situated in geographically distant locations of a city and have varying traveling patterns. To achieve this, we first propose a solution to determine optimal meeting location for two moving users in the Euclidean space. Then, we generalize the problem by considering variable number of moving users and evaluate optimal meeting point on the road network. We extend the work of Yan et al. (Proc VLDB Endow 4(11):1–11, 2011) in this domain by incorporating some real life constraints like variable number of users, varying travel patterns, flexible meeting point and considering road network distance. Experiments are performed on a real-world dataset and show that our method is effective in stated conditions.
1 citations
References
More filters
01 Jan 1985
TL;DR: This book offers a coherent treatment, at the graduate textbook level, of the field that has come to be known in the last decade or so as computational geometry.
Abstract: From the reviews: "This book offers a coherent treatment, at the graduate textbook level, of the field that has come to be known in the last decade or so as computational geometry...The book is well organized and lucidly written; a timely contribution by two founders of the field. It clearly demonstrates that computational geometry in the plane is now a fairly well-understood branch of computer science and mathematics. It also points the way to the solution of the more challenging problems in dimensions higher than two."
6,525 citations
21 Sep 2008
TL;DR: An approach based on supervised learning to infer people's motion modes from their GPS logs is proposed, which identifies a set of sophisticated features, which are more robust to traffic condition than those other researchers ever used.
Abstract: Both recognizing human behavior and understanding a user's mobility from sensor data are critical issues in ubiquitous computing systems As a kind of user behavior, the transportation modes, such as walking, driving, etc, that a user takes, can enrich the user's mobility with informative knowledge and provide pervasive computing systems with more context information In this paper, we propose an approach based on supervised learning to infer people's motion modes from their GPS logs The contribution of this work lies in the following two aspects On one hand, we identify a set of sophisticated features, which are more robust to traffic condition than those other researchers ever used On the other hand, we propose a graph-based post-processing algorithm to further improve the inference performance This algorithm considers both the commonsense constraint of real world and typical user behavior based on location in a probabilistic manner Using the GPS logs collected by 65 people over a period of 10 months, we evaluated our approach via a set of experiments As a result, based on the change point-based segmentation method and Decision Tree-based inference model, the new features brought an eight percent improvement in inference accuracy over previous result, and the graph-based post-processing achieve a further four percent enhancement
1,054 citations
"Analyzing Travel Patterns for Sched..." refers background or methods in this paper
...The GPS trajectory dataset [6, 8, 9] is a repository of real life data collected by Microsoft Research....
[...]
...GeoLife enables users to share travel experiences using GPS trajectories [3, 6, 8, 9, 14]....
[...]
...In this Section, we first present details about the GPS dataset used [6, 8, 9]....
[...]
...Aggregated over tens of users over several days the data size grows exponentially [8,9]....
[...]
11 Jul 2007
TL;DR: A map-based personalized recommendation system which reflects user's preference modeled by Bayesian Networks (BN) and infers the most preferred item to provide an appropriate service by displaying onto the mini map.
Abstract: As wireless communication advances, research on location-based services using mobile devices has attracted interest, which provides information and services related to user's physical location. As increasing information and services, it becomes difficult to find a proper service that reflects the individual preference at proper time. Due to the small screen of mobile devices and insufficiency of resources, personalized services and convenient user interface might be useful. In this paper, we propose a map-based personalized recommendation system which reflects user's preference modeled by Bayesian Networks (BN). The structure of BN is built by an expert while the parameter is learned from the dataset. The proposed system collects context information, location, time, weather, and user request from the mobile device and infers the most preferred item to provide an appropriate service by displaying onto the mini map.
437 citations
07 Apr 2008
TL;DR: An object's trajectory patterns which have ad-hoc forms for prediction are discovered and then indexed by a novel access method for efficient query processing, which estimates an object's future locations based on its pattern information as well as existing motion functions using the object's recent movements.
Abstract: Existing prediction methods in moving objects databases cannot forecast locations accurately if the query time is far away from the current time. Even for near future prediction, most techniques assume the trajectory of an object's movements can be represented by some mathematical formulas of motion functions based on its recent movements. However, an object's movements are more complicated than what the mathematical formulas can represent. Prediction based on an object's trajectory patterns is a powerful way and has been investigated by several work. But their main interest is how to discover the patterns. In this paper, we present a novel prediction approach, namely The Hybrid Prediction Model, which estimates an object's future locations based on its pattern information as well as existing motion functions using the object's recent movements. Specifically, an object's trajectory patterns which have ad-hoc forms for prediction are discovered and then indexed by a novel access method for efficient query processing. In addition, two query processing techniques that can provide accurate results for both near and distant time predictive queries are presented. Our extensive experiments demonstrate that proposed techniques are more accurate and efficient than existing forecasting schemes.
285 citations
23 Jan 2006
TL;DR: This paper proposes an enhanced collaborative filtering solution that uses location as a key criterion for generating recommendations, and describes preliminary results that indicate the utility of such an approach.
Abstract: Internet-based recommender systems have traditionally employed collaborative filtering techniques to deliver relevant "digital" results to users. In the mobile Internet however, recommendations typically involve "physical" entities (e.g., restaurants), requiring additional user effort for fulfillment. Thus, in addition to the inherent requirements of high scalability and low latency, we must also take into account a "convenience" metric in making recommendations. In this paper, we propose an enhanced collaborative filtering solution that uses location as a key criterion for generating recommendations. We frame the discussion in the context of our "restaurant recommender" system, and describe preliminary results that indicate the utility of such an approach. We conclude with a look at open issues in this space, and motivate a future discussion on the business impact and implications of mining the data in such systems.
238 citations