Day-to-Day Traffic Route-Swapping Model And Algorithm

Aiming at the shortcomings of the existing studies and employing the behavioral dynamics modeling, numerical simulation and mathematical optimization theory, this dissertation conducts a systematical research on three aspects including the day-to-day traffic route-swapping model, swapping algorithm and traffic information release strategy. The main innovations of this dissertation are summarized as follows:(1) By indentifying and testifying two behavioral deficiencies, i.e., the weak robustness and over-swapping problem, in the classical proportional-switch adjustment process, a nonlinear pairwise swapping dynamics (NPSD) is proposed based on the pairwise route swapping behavior criterion. Along the analogous line, many other route swapping behaviors, affected by multi-day former experiences as well as conducted by bounded rationality and shortest-path-pursed swapping behavior criteria, are further investigated, and the corresponding multi-day NPSD (MNPSD), bounded-rationality NPSD (BRNPSD) and nonlinear min-cost-pursued swapping dynamics (NMSD) are proposed. It is verified that:i) the above four route swapping dynamics (i.e., NPSD, MNPSD, BRNPSD and NMSD) can avoid the weak robustness and over-swapping problem indentified in PAP, which makes the solutions of these swapping dynamics invariant; in addition, their stationary route traffic flow patterns are equivalent to their traffic equilibria; ii) NPSD and NMSD are both rational behavior adjustment process and their continuous-time versions are globally convergent. The numerical results suggest that:i) traverller’s reaction sensitivity has significant effect on the network traffic evolution process and result; ii) too much dependence on experience can increase the risk of instability; iii) the traffic evolution stationarity under bounded rationality is not always stronger than that under perfect rationality; iv) the stationarity of DNPSD is superior to that of DNMSD, and the former is more applicable to develop swapping algorithm to solve the traffic equilibrium models.(2) By introducing a risk-preference route travel time measure named mean-below travel time (MBTT), and making convex combination between it and the risk-preference mean-excess travel time (METT) measure, a full-risk-attitude route travel time measure named combined mean travel time (CMTT) is established and included into the framework of reliability measure (RM). The RM-based NPSD (RMNPSD) is presented and analyzed. Then a backtracking process associated with reaction sensitivity is further embedded into RMNPSD, deriving a novel swapping algorithm named the nonlinear pairwise swapping algorithm (NPSA) to indentify the RM-based traffic equilibrium. It is found that NPSA needs not the feasible trial and error process for the key parameter; also it is derivative-free and needs not consuming time to find the iteration direction and step length. The numerical results indicate that NPSA has good computing efficiency and can serve as a quick method to find a good initial point for local optimization algorithms.(3) Based on the assumption that travelers are of bounded perception-memory, a day-to-day expost traffic information tailoring strategy and its dynamical programming model are proposed and established. Considering the objective function of the model is nonlinear, non-derivative and has not a close explicit expression, here it is viewed as a stationary programming and a revised compass direct search algorithm is proposed to solve it. Numerical results based on a simple actual traffic network indicate that the expost traffic information tailoring strategy can improve the network evolution efficiency and the revised algorithm performs well.