This article models a novel driving-day-based tradable credit scheme (DD-TCS) to alleviate urban traffic congestion. In this model, car-using allowances (in terms of the number of days in a month, termed as "cred...This article models a novel driving-day-based tradable credit scheme (DD-TCS) to alleviate urban traffic congestion. In this model, car-using allowances (in terms of the number of days in a month, termed as "credit") are freely and uniformly allocated to all travellers, who are also allowed to trade them in a market according to his/her travel needs (e.g. driving more or fewer days than the free endowment). As opposed to most studies on TCS, this paper explicitly considers the transaction cost (e.g. infor-mation cost of finding potential traders) in the trading market. To assess the feasibility of DD-TCS, we compare it against the license plate rationing (LPR) scheme, which has been practically implemented in many cities such as Beijing and Chengdu in China. Taking the performance of LPR as a benchmark, we quantify the threshold values of the transaction cost in DD-TCS when the two schemes yield equivalent performance (in terms of the total gener-alized cost). In numerical studies, we also compare the DD-TCS and LPR with the no-action case and the congestion pricing case (representing the theoretical optimum). Results show that both DD-TCS and LPR outperform the no-action case under certain conditions. With small trans-action cost, DD-TCS may achieve a lower system cost that can be very close to the ideal optimum. In addition, parameter analysis shows that DD-TCS performs better than LPR in a wide range of transaction cost, where the threshold values appear to account for a considerable portion of the auto travel time. This implies that DD-TCS will be more appealing than LPR in practice because a transaction cost lower than the extremely large threshold values can be easily achieved for the trading market, e.g. via a mobile platform and modern communication techniques.展开更多
基金supported by the National Natural Science Foundation of China (Project No.51608455)
文摘This article models a novel driving-day-based tradable credit scheme (DD-TCS) to alleviate urban traffic congestion. In this model, car-using allowances (in terms of the number of days in a month, termed as "credit") are freely and uniformly allocated to all travellers, who are also allowed to trade them in a market according to his/her travel needs (e.g. driving more or fewer days than the free endowment). As opposed to most studies on TCS, this paper explicitly considers the transaction cost (e.g. infor-mation cost of finding potential traders) in the trading market. To assess the feasibility of DD-TCS, we compare it against the license plate rationing (LPR) scheme, which has been practically implemented in many cities such as Beijing and Chengdu in China. Taking the performance of LPR as a benchmark, we quantify the threshold values of the transaction cost in DD-TCS when the two schemes yield equivalent performance (in terms of the total gener-alized cost). In numerical studies, we also compare the DD-TCS and LPR with the no-action case and the congestion pricing case (representing the theoretical optimum). Results show that both DD-TCS and LPR outperform the no-action case under certain conditions. With small trans-action cost, DD-TCS may achieve a lower system cost that can be very close to the ideal optimum. In addition, parameter analysis shows that DD-TCS performs better than LPR in a wide range of transaction cost, where the threshold values appear to account for a considerable portion of the auto travel time. This implies that DD-TCS will be more appealing than LPR in practice because a transaction cost lower than the extremely large threshold values can be easily achieved for the trading market, e.g. via a mobile platform and modern communication techniques.
