Modelling And Simulation Of Service Design Considering Demand Responsive Transit In Different Operational Scenarios

With the rapid urbanization and development of economy,the number of private cars in households has increased dramatically.Besides,the gradual networking of urban rail systems leads to a decreasing demand for traditional transit.As a result,bus operators need to come up with new travel modes that are more sensitive to the actual operating scenarios and passenger demand in order to improve the attractiveness to passengers.Combined with the development of future mobility,on the one hand,bus operation will put forward more suitable strategies according to the special situation and operational scenarios.On the other hand,the mobility of people and goods service will increasingly tend to be electric,so as to reduce the impact on environmental pollution.In this context,we propose the service design(i.e.line design,timetable and vehicle scheduling)for two operating scenarios based on the demand responsive transit(DRT)travel mode in this paper: the resumption of work after the COVID-19 epidemic and electric buses.The method of static modelling and dynamic simulation are designed to analyze and evaluate the set of indicators,so that the operators can make the reasonable decisions.For the first scenario,this paper investigates the bus operation mode during the resumption phase under the impact of the epidemic.Taking into account the premise of bus operation and travel safety,different social distancing measures are provided with the fixed loading index based on the DRT mode.By collecting the spatial and temporal passenger demand information in advance,a clustering algorithm is applied to determine the spatial location of pick-up points,and the corresponding departure times is obtained according to the loading index with different social distancing measures.With the extracted time points of arrival node and departure mode,the model based on the deficit function is formulated,and the optimal vehicle scheduling with the minimum fleet size is get by inserting the deadheading process with the constraint of vehicle departure time.For the second scenario,this paper investigates the DRT operation scenario under electric buses.The pick-up time is obtained by setting a fixed pick-up strategy,and the corresponding decision time point is used to build the simulation framework(energy consumption simulation and vehicle scheduling simulation): the remaining energy and the vehicle location are defined as the state variables,and the vehicle routing scheme(pick-up route and recharging route)is developed according to the pre-determined vehicle scheduling strategy based on pick-up time constraint and energy constraint.With the given demand distribution and study area(network),a system of indicators based on passenger service and bus operation is established to analyze and evaluate the different social distancing measures,pick-up strategies,dispatching strategies and recharging strategies.According to the experimental results of the first scenario,as the loading index decreases,the total vehicle travel time increases,even though the average passenger waiting time is decreasing.When passenger demand is uniformly distributed,the minimum number of vehicles in the system increases with decreasing trend of loading index.Under the influence of epidemic,it is necessary for operators to consider the impact of different indicators and develop a reasonable service design with different social distancing measures.For the second scenario,different output metrics are obtained by varying different pick-up strategies,dispatching strategies and recharging strategies.It is observed that there is a strong correlation between each two indicators of the the number of trips,level of service,energy consumption and fleet size.Furthermore,the fleet size and energy consumption can be reduced as the turnover rate of vehicles in the system accelerates.