Understanding and Mitigating Capacity Reduction at Freeway Bottlenecks
Two freeway bottlenecks, each with a distinct geometry, have been investigated in an effort to understand traffic conditions leading to capacity losses (i.e., breakdown). One bottleneck is formed by a horizontal curve and the other by a reduction in travel lanes. These bottlenecks are shown to exhibit breakdowns after queues form immediately upstream. The vehicle accumulations that arise near these bottlenecks are shown to be good proxies for the mechanisms that trigger breakdowns. Evidence is provided to show that these losses can be recovered, postponed or even avoided entirely by controlling the accumulations. An algorithm for estimating vehicle accumulations has been developed in this dissertation. This algorithm? estimates are obtained from the counts made by ordinary detectors (e.g. inductive loops) placed in series. The accumulations estimated are those that arise on the intervening (freeway) segments between the detectors. These estimates can be obtained in real-time at short intervals of a second or so. The systematic errors (i.e., bias) that invariably arise in detector counts are automatically corrected when traffic is freely flowing. The algorithm is thus well suited for monitoring accumulations near a bottleneck prior to capacity drops and the estimates it furnishes can, in turn, dictate control actions (e.g. metering rates) that prolong higher outflows from the bottleneck. The estimates that the algorithm furnishes can also be used for incident detection and delay estimation.