Multimap Routing for Road Traffic Management Alvaro Paricio Garcia 1[0000-0002-9162-4147] and Miguel A. Lopez-Carmona 2[0000-0001-9228-1863] Universidad de Alcala, Madrid, Spain. http://www.uah.es ⋆ alvaro.paricio@uah.es, miguelangel.lopez@uah.es Abstract. TWM -Traffic Weighted Multi-maps- is presented as a novel traffic route guidance model to reduce urban traffic congestion, focus- ing on individual trip and collective objectives considering citizens, in- dividual multi-modal mobility, and heterogeneous traffic groups. They have different interests, goals and regulation, so new multi-objective cost functions and control systems are required. TWM is structured around a novel control paradigm, based on the generation and distribution of com- plementary cost maps for traffic collectives (fleets), oriented towards the application of differentiated traffic planning and control policies. Agents receive a customized view TWM of the network that is used to calculate individual route using standard means and tools. The research describes the TWM theoretical model and microscopic simulations over standard reference traffic network grids, different traffic congestion scenarios, and several driver’s adherences to the mechanism. Travel-time results show that TWM can have a high impact on the network performance, leading to enhancements from 20% to 50%. TWM is conceived to be compatible with existing traffic routing systems. The research has promising future evolution applying new algorithms, policies and network profiles. Keywords: Dynamic Traffic Assignment · Traffic Control · Traffic Sim- ulation · Vehicle Routing · Traffic Big Data · Decision Making · Multi- agent systems 1 Introduction One of the main challenges in the modeling and design of traffic management systems and services is the difficulty of controlling driver’s decision making re- garding the choice of their routes, in order to match resources and demand in an optimal and automated way. Currently, Traffic Control System (TCS) coor- dinate demand through direct intervention in the network, online information systems, panels, regulatory policies or restrictions [21]. Drivers, for their part, are increasingly using advanced agent-based navigation systems that adapt and react in real time to the state of traffic [20]. Thus, the majority of vehicles receive very similar recommendations and stimuli, which make it difficult to optimize demand and transfer situations of congestion [22, 16]. ⋆ This work was supported in part by the Spanish Ministry of Economy and Compet- itiveness under Grant TIN2016-80622-P and Grant TEC2013-45183-R.