Traffic wave theory is used to study the critical conditions for traffic jams according to their features. First, the characteristics of traffic wave propagation is analyzed for the simple signal-controlled lane and t...Traffic wave theory is used to study the critical conditions for traffic jams according to their features. First, the characteristics of traffic wave propagation is analyzed for the simple signal-controlled lane and the critical conditions for oversaturation is established. Then, the basic road is decomposed into a series of one-way links according to its topological characteristics. Based on the decomposition, traffic wave propagation under complex conditions is studied. Three complicated factors are considered to establish the corresponding critical conditions of jam formation, namely, dynamic and insufficient split, channelized section spillover and endogenous traffic flow. The results show that road geometric features, traffic demand structures and signal settings influence the formation and propagation of traffic congestion. These findings can serve as a theoretical basis for future network jam control.展开更多
With the rapid development of location-based services, a particularly important aspect of start-up marketing research is to explore and characterize points of interest （PoIs） such as restaurants and hotels on maps. ...With the rapid development of location-based services, a particularly important aspect of start-up marketing research is to explore and characterize points of interest （PoIs） such as restaurants and hotels on maps. However, due to the lack of direct access to PoI databases, it is necessary to rely on existing APIs to query Pols within a region and calculate PoI statistics. Unfortunately, public APIs generally im- pose a limit on the maximum number of queries. Therefore, we propose effective and efficient sampling methods based on road networks to sample PoIs on maps and provide unbiased estimators for calculating PoI statistics. In general, the more intense the roads, the denser the distribution of PoIs is within a region. Experimental results show that compared with state-of-the-art methods, our sampling methods improve the efficiency of aggregate statistical estimations.展开更多
基金The National Basic Research Program of China(973 Program)(No.2006CB705505)the Basic Scientific Research Fund of Jilin University(No.200903209)
文摘Traffic wave theory is used to study the critical conditions for traffic jams according to their features. First, the characteristics of traffic wave propagation is analyzed for the simple signal-controlled lane and the critical conditions for oversaturation is established. Then, the basic road is decomposed into a series of one-way links according to its topological characteristics. Based on the decomposition, traffic wave propagation under complex conditions is studied. Three complicated factors are considered to establish the corresponding critical conditions of jam formation, namely, dynamic and insufficient split, channelized section spillover and endogenous traffic flow. The results show that road geometric features, traffic demand structures and signal settings influence the formation and propagation of traffic congestion. These findings can serve as a theoretical basis for future network jam control.
基金This work was partially supported by the National Natural Science Foundation of China （NSFC） （Grant N os. 61170020, 61402311, 61440053）, and the US National Science Foundation （IIS- 1115417）.
文摘With the rapid development of location-based services, a particularly important aspect of start-up marketing research is to explore and characterize points of interest （PoIs） such as restaurants and hotels on maps. However, due to the lack of direct access to PoI databases, it is necessary to rely on existing APIs to query Pols within a region and calculate PoI statistics. Unfortunately, public APIs generally im- pose a limit on the maximum number of queries. Therefore, we propose effective and efficient sampling methods based on road networks to sample PoIs on maps and provide unbiased estimators for calculating PoI statistics. In general, the more intense the roads, the denser the distribution of PoIs is within a region. Experimental results show that compared with state-of-the-art methods, our sampling methods improve the efficiency of aggregate statistical estimations.
