New Approaches for Traffic Management in Metropolitan Areas

Kühne, Reinhart; Scbäfer, R.-P.; Mikat, Jürgen; Thiessenhusen, Kai-Uwe; Böttger, Ute; Lorkowski, Stefan

doi:10.1016/s1474-6670(17)32421-7

Cited by 19 publications

(10 citation statements)

References 1 publication

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“…The proliferation of these devices has enabled the collection of huge amount of GPS trajectories. More and more applications, such as route planner [7], hot route finder [16], traffic flow analysis [15], geographical social network [23], have started to use information from GPS data to achieve better quality of services.…”

Section: Introductionmentioning

confidence: 99%

Map-matching for low-sampling-rate GPS trajectories

Lou

Zhang

Zheng

et al. 2009

Proceedings of the 17th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems

672

464

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Map-matching is the process of aligning a sequence of observed user positions with the road network on a digital map. It is a fundamental pre-processing step for many applications, such as moving object management, traffic flow analysis, and driving directions. In practice there exists huge amount of low-samplingrate (e.g., one point every 2-5 minutes) GPS trajectories. Unfortunately, most current map-matching approaches only deal with high-sampling-rate (typically one point every 10-30s) GPS data, and become less effective for low-sampling-rate points as the uncertainty in data increases. In this paper, we propose a novel global map-matching algorithm called ST-Matching for lowsampling-rate GPS trajectories. ST-Matching considers (1) the spatial geometric and topological structures of the road network and (2) the temporal/speed constraints of the trajectories. Based on spatio-temporal analysis, a candidate graph is constructed from which the best matching path sequence is identified. We compare ST-Matching with the incremental algorithm and Average-Fré chet-Distance (AFD) based global map-matching algorithm. The experiments are performed both on synthetic and real dataset. The results show that our ST-matching algorithm significantly outperform incremental algorithm in terms of matching accuracy for low-sampling trajectories. Meanwhile, when compared with AFD-based global algorithm, ST-Matching also improves accuracy as well as running time.

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Section: Introductionmentioning

confidence: 99%

Map-matching for low-sampling-rate GPS trajectories

Lou

Zhang

Zheng

et al. 2009

Proceedings of the 17th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems

672

464

View full text Add to dashboard Cite

show abstract

“…Recently, GPS-enabled vehicles have been widely adopted to collect real time traffic data (Dai, Ferman, & Roesser, 2003;Kühne et al, 2003;Lü, Zhu, Wu, Dai, & Huang, 2008;Tong, Coifman, Merr, 2009). In many applications, such data are often obtained from GPS-enabled taxis instead of private automobiles due to privacy concerns.…”

Section: Introductionmentioning

confidence: 99%

Urban land uses and traffic ‘source-sink areas’: Evidence from GPS-enabled taxi data in Shanghai

Wang

Xiao

et al. 2012

Landscape and Urban Planning

384

201

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“…Definition 4. The road network topology The network topology structure is denoted as G(R n )=(V, E), where V=L(R n ) indicates the link set of R n and E is the connectivity relation between the links which is established according to the (2) connectivity of the roads.…”

Section: Road Network Topologymentioning

confidence: 99%

“…Its basic principle is to periodically record the location, direction and speed information of the traveling vehicle from the floating car with the data of the Global Position System (GPS). The information can be processed by the related computing model and algorithm such as the path-estimating and road traffic congestion information, so that the floating car data can be associated with the city road in real time, finally the road congestion information such as driving speed of the floating car and the possible traveling time of the road can be obtained [2] .…”

Section: Introductionmentioning

confidence: 99%

A heuristic path-estimating algorithm for large-scale real-time traffic information calculating

Lü

Zhu

et al. 2008

Sci. China Ser. E-Technol. Sci.

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As the original Global Position System (GPS) data in Floating Car Data have the accuracy problem, this paper proposes a heuristic path-estimating algorithm for large-scale real-time traffic information calculating. It uses the heuristic search method imports the restriction with geometric operation, and makes comparison between the vectors composed of the vehicular GPS points and the special road network model to search the set of vehicular traveling route candidates. Finally, itchooses the most optimal one according to weight. Experimental results indicate that the algorithm has considerable efficiency in accuracy (over 92.7%) and computational speed (max 8000 GPS records per second) when handling the GPS tracking data whose sampling rate is larger than 1 min even under complex road network conditions.

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New Approaches for Traffic Management in Metropolitan Areas

Cited by 19 publications

References 1 publication

Map-matching for low-sampling-rate GPS trajectories

Map-matching for low-sampling-rate GPS trajectories

Urban land uses and traffic ‘source-sink areas’: Evidence from GPS-enabled taxi data in Shanghai

A heuristic path-estimating algorithm for large-scale real-time traffic information calculating

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