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Constrained route planning based on the regular expression

WANG Jing, LIU Hui-ping, JIN Che-qing

Journal of East China Normal University(Natural Sc 2017, 2017 (5): 162-173,235. DOI: 10.3969/j.issn.1000-5641.2017.05.015

Abstract （364）

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Traditional route planning algorithms, which mainly focus on metrics such as the distance, time, cost, etc. to find the optimal route from source to destination, are not suitable for solving route planning requirements with location constraints. For example, finding the shortest path passing the whole or a part of user-defined location categories in order or disorder. Mainly focusing on these scenarios, this paper formalizes the constrained route planning problem on the basis of the regular expression generated by user requirements and gives a general framework to solve this problem. Based on this, a basic constrained route planning algorithm (BCRP) and an improved constrained route planning algorithm (ICRP) are proposed while ICRP reduces the search space using pruning rules. Finally, extensive experiments on real road network datasets demonstrate the efficiency of our proposal.

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Privacy preserving method of spatio-temporal data based on k-generalization technology

YANG Zi, NING Bo, LI Yi

Journal of East China Normal University(Natural Sc 2017, 2017 (5): 174-185. DOI: 10.3969/j.issn.1000-5641.2017.05.016

Abstract （383）

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In recent years, more and more devices based on location system, resulting in a large amount of location information by the mobile device users to access and use, from the perspective of data mining, the data is of immeasurable value, but in terms of personal privacy, people don't want their information to be leaked and used to sparked strong privacy concerns. At present, many papers have proposed privacy protection technology to solve this problem. Generally speaking, there are several categories of interference, suppression and generalization. In order to protect the privacy of personal spatio-temporal data, this paper proposes a method of k-generalization. To limit the scope of the user may appear, improve the availability of data; selection of nodes to generalization so that the user's maximum security; considers multiple sensitive node solutions exist under the condition, and for the purpose of improving the data utility on a number of sensitive nodes are optimized. Finally, the performance of the algorithm is evaluated by experiments, and it is proved that the algorithm is effective to protect personal privacy.

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Top-k hotspots recommendation algorithm based on real-time traffic

WU Tao, MAO Jia-li, XIE Qing-cheng, YANG Yan-qiu, WANG Jin

Journal of East China Normal University(Natural Sc 2017, 2017 (5): 186-200. DOI: 10.3969/j.issn.1000-5641.2017.05.017

Abstract （333）

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To cut down the no-load rate of taxis and relieve the traffic pressure, an effective hotspot recommendation method of picking up passenger is necessitated. Aiming at the problem of lower recommendation precision of traditional recommendation technique due to ignoring the actual road situation, we propose a two-phase real-time hotspot recommendation approach for picking up passenger. In the phase of offline mining, timebased hotspots are extracted by mining the history taxi trajectory dataset. In the phase of online recommendation, according to the position and time of taxi requests, a potential no-passenger time cost evaluation function that based on real-time road situation is presented to evaluate and rank hotspots, and obtain top-k hotspots of picking up passenger.Experimental results on taxi trajectory data show that, our proposal ensure smaller potential no-load time overhead due to considering real-time traffic conditions, and hence has good effectiveness and robustness as compared to the traditional recommendation approached.

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Individual station estimation from smart card transactions

WANG Yi-lin, ZHANG Zhi-gang, JIN Che-qing

Journal of East China Normal University(Natural Sc 2017, 2017 (5): 201-212. DOI: 10.3969/j.issn.1000-5641.2017.05.018

Abstract （495）

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With the fast development of public transportation network and widespread use of smart card, more and more rich semantic information about human mobility behaviors are hidden in smart card transaction data. However, a great number of current smart cards are initially designed for charging and do not record any detailed information about where and when a passenger gets on or gets off a bus, which brings out great difficulties for analyzing, mining transaction data and providing more precise location-based services. This paper presents Space-Time Adjacency algorithm (STA) and Historical Trip Based algorithm (HTB) to estimate the bus station of each card's transaction records with the aid of integral historical data including complete subway transaction data. Specifically, STA does the initial reconstruction work according to the space-time proximity of adjacent transaction records. Then HTB first cuts the collection of records to form trips that contain explicit trip purposes, then extracts taken lines and transfer lines using historical data, next generates candidate stations for each taken line, and finally uses them to recover the transaction records again. Experiments show that the proposed algorithms work well and narrow the range of candidate stations for bus lines, and have good time efficiency.

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