On the variance of recurrent traffic flow for statistical traffic assignment

Measuring and reducing the disequilibrium levels of dynamic networks with ride-sourcing vehicle data

“…As a result, the NDL is attributed to travelers' inability to know what the shortest-time route is. Many other possible causes of NDL exist [34,49,12,32,59]. This paper will focus on evaluating and reducing the NDL first, while exploring the causes of NDL will be left for future research.…”

“…Zhu and Levinson [64], Jan et al [22] conducted empirical studies using GPS data and found that most travelers do not follow shortest paths in the network, and some studies indicate that travelers are likely to follow the shortest distance paths [1,44] or hyperpaths [30]. Studies also explored alternative models with milder assumptions, such as bounded rationality [34,12], statistical traffic equilibrium [38,32], and mean-excess traffic equilibrium [7].…”

Statistical inference of probabilistic origin-destination demand using day-to-day traffic data

“…Other studies [30,82,24] show that the variance/covariance matrix of the O-D demand have a significant influence on network traffic conditions. Though adopting stochastic O-D demand, those traffic assignment models (except Clark and Watling [19]) assumed non-atomic (infinitesimal) players and therefore are unable to capture the stochasticity of route choices that vary from day to day (the proof is shown in Ma and Qian [48]). Classical UE, SUE and RUE are all deterministic route choice models where the number of (infinitesimal) players assigned to each route is fixed, rather than being stochastic.…”

“…Though route choices are stochastic, these studies did not work directly with the covariance of demand among all O-D pairs. A detailed comparison of those assignment models is further illustrated in Ma and Qian [48].…”

Optimization of Linear Traffic Distribution Problem in Terms of the Road Toll Structure Assuming an Autonomous Transportation System

“…An important objective of new innovative mobility solutions is to facilitate accessibility of transpor t systems and i mprov ing integration of new concepts (Přibyl and Svítek, 2015), hence the paper aims to develop a new linear optimization model (Hu and Kahng, 2016), which makes it possible to allocate travel demand to transport network components assuming the existence of a smart autonomous transport system maximizing operation efficiency of the network . Traffic distribution problem has been discussed in scientific researches (Ryu et al, 2017;Ma and Qian, 2017), but these are mainly limited on business oriented fields such as freight transportation and logistics (Ansari et al, 2017) and rarely considering passenger transportation systems and the constrained infrastructure capacities. In our research, a well adaptable linear programming approach has been implemented to determine static system optimum of the traf f ic distribution problem, as linear approach proved to be useful for estimating traffic volumes (Apronti et al, 2016) as well as for optimizing infrastructural elements (Kurczveil and Becker, 2016).…”