Abstract
Identifying commuting patterns for an urban network is important for various traffic applications (e.g., traffic demand management). Some studies, such as the gravity models, urban-system-model, K-means clustering, have provided insights into the investigation of commuting pattern recognition. However, commuters’ route feature is not fully considered or not accurately characterized. In this study, a systematic framework considering the route feature for commuting pattern recognition was developed for urban road networks. Three modules are included in the proposed framework. These modules were proposed based on automatic license plate recognition (ALPR) data. First, the temporal and spatial features of individual vehicles were extracted based on the trips detected by ALPR sensors, then a hierarchical clustering technique was applied to classify the detected vehicles and the ratio of commuting trips was derived. Based on the ratio of commuting trips, the temporal and spatial commuting patterns were investigated, respectively. The proposed method was finally implemented in a ring expressway of Kunshan, China. The results showed that the method can accurately extract the commuting patterns. Further investigations revealed the dynamic temporal-spatial features of commuting patterns. The findings of this study demonstrate the effectiveness of the proposed method in mining commuting patterns at urban traffic networks.
Highlights
The commuting traffic contributes a lot to traffic congestion, air pollution and greenhouse gas emissions [1]
Varga [3] proposed a further generalization for the original radiation model-flow and jump model (FJM); test results showed that the FJM can offer an improved description for commuting data
A ring expressway with 35 on-ramps and 24 off-ramps in Kunshan City, China was selected as the test siteF.eaAtusre tEhxetrascetiloencted network is a closed ring expressway network, the vehicle path caAn rbinegdeexpterersmswinayedwibthy35anono-rraimgpins aanndd24aodff-ersamtinpsatinioKnu.nsThhanerCeiftyo,rCeh, ionna lwyasthseeleoctreidgians and destination infotvhreemhitcealsettisopitnaeth(wasceasrnheobwuensdeiendteFtriomguidnreeed2s)c.brAyibsaetnhetohsreeiglesicnpteaadntindaeltawfdeoearksttuiinsraaetsicolonos.feTdahrteirnreagfvoerexepl,eroer.snslTwy hatyheenAeotLrwigPoirRnk,adtnhedevices were located at doenst-irnaamtiopnsinafnordmoatffio-nrawmerpesuosefdthtoedseeslcercibteedthenesptwatioalrkfe.atures of a traveler
Summary
The commuting traffic contributes a lot to traffic congestion, air pollution and greenhouse gas emissions [1]. Limited by traffic data availability in large-scale road networks, these studies estimated the commuting flows at zonal or regional levels based on demographic information, distances between traffic zones, land use, and so on. These models are all traffic planning-oriented and cannot be used to derive commuting patterns for the purpose of traffic management. Large sample size, and real-time data availability of ALPR data [28], these studies highlighted their potentials in individual level traffic pattern recognition These data-driven studies added empirical evidence to commuting pattern recognition from different aspects with a series of data mining methods.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
