适应性之动态测重站控制策略(英文)

Adaptive WIM strategy for weigh station control

公路管理机构常用动态测重技术(WIM)来减少车辆滞留和执行超重车辆通行法.一般的做法是采用固定阈值(即合法重量极值)来初步过滤和分类车辆.如果某卡车的动态测重读数超过该阈值,该卡车会被指示去静态测重作进一步的检查,如:超重属实则可能开出处罚传票.适应性测重阈值法能充分利用动态测重技术并增强其操作效率.当在测重站等候的车辆很多时,阈值将增大,以避免测站阻塞,同时仍然可以逮住严重超重的违规车辆.当等候车辆少时,该阈值降低,以尽量多地检查车辆.主要阐述对适应性测重法的操作和设计的估算,并建立了一个高速路匝道出口动态测重站的微观模拟模型.然后,模拟和测试了动态阈值调节策略.结果表明,在广泛的条件下,适应性法总是优于传统的静态阈值操作法.在交通流量需求很大条件下,该算法抓住的超重卡车比静态操作的多44%以上,同时,其平均车辆滞留只为在静态操作情况下的30%.换句话说,所提出的适应性测重法更有效地控制了超重并满足了交通流量的要求.

Weigh-in-Motion （WIM） technology is commonly deployed to reduce delay and aid enforcement of overweight vehicles. A fixed threshold value, the legal weight limit, is used to filter and sort trucks by weight. If the WIM reading of a truck is over this threshold value, the truck could be directed to a static scale for further inspection and citation. An adaptive weight threshold algorithm was devised to fully utilize the WIM technology and enhance the operational efficiency at WIM-equipped sites. The threshold value is increased when the queue at the weigh station is long so as to avoid the closure of the weigh station while still catching the worst weight offenders. The threshold value is lowered when the queue at the station is short to maximize the number of trucks inspected. This paper focuses on the evaluation of the design and operations of the adaptive weight algorithm. A microscopic simulation model based on a real-world ramp WINI weigh station site was established and calibrated. Subsequently, the dynamic threshold adjustment strategy was modeled and tested. The results show that adaptive algorithm is always better than the traditional static threshold operations under a wide range of conditions. Under heavy demand conditions, the algorithm catches 44% more overweight trucks than the static operations while keeping the average delay per legal-weight truck at only 30% of that under static operations. In other words, the proposed adaptive weight algorithm is both more effective in weight enforcement and more efficient in serving the traffic demand.