Recently, an open-source light-weight dynamic traffic assignment (DTA) package, namely DTALite, has been developed to allow a rapid utilization of advanced dynamic traffic analysis capabilities. Aiming to bridge the modeling gaps between multi-agent simulation and optimization in a multimodal environment, we further design and develop DTALite-S to simplify the traffic flow dynamic representation details in DTALite for future extensions. We hope to offer a unified modeling framework with inherently consistent space–time network representations for both optimization formulation and simulation process. This paper includes three major modeling components: (1) mathematic formulations to describe traffic and public transportation simulation problem on a space–time network; (2) transportation transition dynamics involving multiple agents in the optimization process; (3) an alternating direction method of multipliers (ADMM)-based modeling structure to link different features between multi-agent simulation and optimization used in transportation. This unified framework can be embedded in a Lagrangian relaxation method and a time-oriented sequential simulation procedure to handle many general applications. We carried out a case study by using this unified framework to simulate the passenger traveling process in Beijing subway network which contains 18 urban rail transit lines, 343 stations, and 52 transfer stations. Via the ADMM-based solution approach, queue lengths at platforms, in-vehicle congestion levels and absolute deviation of travel times are obtained within 1560 seconds.The case study indicate that the open-source DTALite-S integrates simulation and optimization procedure for complex dynamic transportation systems and can efficiently generate comprehensive space-time traveling status.

This paper presents a microscopic traffic simulation approach to the capacity impact analysis of weaving sections caused by the installation of exclusive bus lanes (XBLs) on urban expressways. Three typical configurations of XBL in Beijing are identified, including median bus lane with off-on-ramp, curbside bus lane with on-off-ramp, and curbside bus lane with off-on-ramp. Then, the weaving section length, headway, mainline volume, as well as off-ramp and on-ramp volumes for general traffic are considered as parameters in designing simulation scenarios. Finally, the microscopic simulation is executed, by which the capacity of general traffic for weaving sections is estimated and the impact of the weaving section length and headway on the capacity is evaluated. It is found that the weaving section length and headway have different impacts on the capacity of general traffic in different configurations. The results also provide that the weaving section length and headway are more sensitive in the on-off-ramp than in the off-on-ramp scenario with the curb bus lane.

Computational graphs (CGs) have been widely utilized in numerical analysis and deep learning to represent directed forward networks of data flows between operations. This paper aims to develop an explainable learning framework that can fully integrate three major steps of decision support: Synthesis of diverse traffic data, multilayered traffic demand estimation, and marginal effect analyses for transport policies. Following the big data-driven transportation computational graph (BTCG) framework, which is an emerging framework for explainable neural networks, we map different external traffic measurements collected from household survey data, mobile phone data, floating car data, and sensor networks to multilayered demand variables in a CG. Furthermore, we extend the CG-based framework by mapping different congestion mitigation strategies to CG layers individually or in combination, allowing the marginal effects and potential migration magnitudes of the strategies to be reliably quantified. Using the TensorFlow architecture, we evaluate our framework on the Sioux Falls network and present a large-scale case study based on a subnetwork of Beijing using a data set from the metropolitan planning organization.

The Beijing 2008 Summer Olympic Games was held from August 8 to 24, 2008, which definitely drew attention from all walks of life at home and abroad. Beijing 2008 Olympic Games was a great event, and there were huge demands for transportation services. How to coordinate the huge Olympic transportation demand of spectator and daily travel demand from local residents is one of the most important considerations for Olympic transportation agency. The Beijing Olympic transport model (BOTM) was designed to provide a tool for analyzing travel demand and forecasting traffic operation conditions during Olympic Games. This paper starts to search for the travel characteristics of local Beijing residents and spectators in game time by dividing travel demands into two parts: base demand and Olympic demand. Then, activity-chain-based forecasting methodology was used to develop model. BOTM included base model and game model by applying VISEM and EXCEL software packages. Finally, transportation demand management measures ...

To properly reflect the interdependency mechanism between transport networks and trip-making behavior, an integrated travel demand model with feedback mechanism is established for Beijing, a typical developing mega-city. Compared with the conventional sequentially estimated four-step model, it is empirically confirmed that introduction of the feedback mechanism has greatly improved the model estimation accuracy. Moreover, the simulation results suggest that development of transit-oriented urban form with proper control of car ownership and increasing public transit lines would be a promising scenario to realize environmentally sustainable urban growth for Beijing.