A Tradable Credit Scheme (TCS) is a demand management policy aiming for more sustainable travel behavior. The regulator defines the total credit cap and the credit distribution; it also determines the credit charges for each travel alternative at different times of the day, which modifies the perceived users’ costs. The credit price is determined by trading of credits between travelers. Defining the credit scheme at the urban level and estimating its impacts on user travel decisions and the network congestion dynamics is challenging. We propose a framework wherein travelers change their departure times and choose between solo car driving, Public Transportation (PT), and carpooling to complete their trips under a dynamic TCS, meaning the credit charge is time-dependent. A multimodal macroscopic traffic simulator based on a generalized bathtub model captures the congestion dynamics for the different transport modes. Additionally, we consider different values of time, trip lengths, and desired arrival times for the demand profile.

The proposed TCS minimizes the total travel cost, (the sum of all travelers’ travel costs) and the carbon emissions by solving an optimization problem through an iterative method, including an inner loop that updates the users’ choices and credit price under the stochastic user equilibrium principle; and an outer loop that updates the credit charge profile. The methodology is implemented and applied to a realistic test case in Lyon (France). The dynamic TCS profiles result in 36% fewer carbon emissions than static TCS for a total travel cost reduction of 19%. Besides, 94% of the travelers benefit from the TCS as their user costs decrease in the case study. The final results are compared with a more advanced simulation framework, which is too computationally expensive to find the optimal TCS. The proposed TCS is effective with refined traffic dynamics representation.

可交易信贷计划 (TCS) 是一种需求管理政策，旨在实现更可持续的旅行行为。监管机构定义总信贷上限和信贷分配；它还确定了一天中不同时间每个旅行选择的信用费用，从而修改了感知用户的成本。积分价格由旅行者之间的积分交易决定。在城市层面定义信用方案并估计其对用户出行决策和网络拥堵动态的影响具有挑战性。我们提出了一个框架，在这个框架中，旅行者可以改变他们的出发时间，并在单独驾车、公共交通 (PT) 和拼车之间进行选择，以在动态 TCS 下完成他们的旅行，这意味着信用费用是时间相关的。基于广义浴缸模型的多模式宏观交通模拟器捕捉不同交通方式的拥堵动态。此外，我们还考虑了不同的时间值、行程长度和需求概况的期望到达时间。

拟议的 TCS 通过迭代方法解决优化问题，将总旅行成本（所有旅行者旅行成本的总和）和碳排放量降至最低，包括在随机用户下更新用户选择和信用价格的内循环均衡原则；以及一个更新信用收费概况的外循环。该方法已在里昂（法国）实施并应用于实际测试案例。动态 TCS 配置文件比静态 TCS 减少了 36% 的碳排放，总旅行成本降低了 19%。此外，在案例研究中，94% 的旅行者受益于 TCS，因为他们的用户成本降低了。将最终结果与更高级的仿真框架进行比较，该框架的计算成本太高，无法找到最佳 TCS。