516 results about "Bus transit" patented technology

The invention relates to the technical field of traffic management, in particular to a smart city intelligent traffic management system based on big data, which comprises a multi-source data acquisition module, a data fusion module, a dynamic feature generation module, a situation prediction module, a multi-target optimization module and an instruction execution module, the multi-source data acquisition module acquires multi-source data in real time; the data fusion module generates a standardized traffic data flow and outputs a three-dimensional data cube; the dynamic feature generation module outputs a traffic feature matrix; the situation prediction module generates a dynamic situation map; the multi-objective optimization module outputs an optimization instruction set; and the instruction execution module issues a control instruction to the traffic signal controller, the variable information board and the vehicle-mounted terminal. According to the invention, traffic jam can be relieved, road traffic efficiency can be improved, bus priority scheduling can be optimized, vehicle delay can be reduced, and emergency response speed can be improved.

The invention discloses a public transport passenger flow monitoring method and system based on human body detection, and the method comprises the steps: synchronously collecting skeleton key points, heat distribution and contour features of passengers through a vehicle-mounted multi-view camera and an infrared sensor, and generating a passenger space-time feature matrix; outputting a passenger movement track set based on the passenger space-time characteristic matrix; generating a dynamic congestion degree index according to the staying duration and the spatial distribution density of the passengers in the passenger movement track set; calculating an abnormal behavior probability based on the dynamic congestion degree index, and outputting an abnormal event early warning signal; and fusing the dynamic congestion degree index, the abnormal event early warning signal and the real-time passenger flow data, and generating a bus passenger flow monitoring result through space-time correlation analysis. According to the embodiment of the invention, high-precision, low-delay and intelligent passenger flow state monitoring and abnormity early warning can be realized.

The invention discloses an electric bus charging and discharging combined scheduling method, and belongs to the field of intelligent bus scheduling. The method comprises the following steps: firstly, collecting an operation plan and time-of-use electricity price data of an electric bus, and then constructing an electric bus participation vehicle network integration charging and discharging scheduling model which comprehensively considers cost benefits; the model can decide the optimal charging and discharging strategy of each electric bus according to the operation plan and the residual electric quantity of the electric bus and the real-time electricity price of a power grid, so that the bus operation requirement is met, the charging at the electricity price valley period and the discharging at the electricity price peak period can be fully realized, and the charging and discharging efficiency is improved. Therefore, the charging cost is reduced and the electricity selling income is maximized. Through the method, the electric bus not only can meet the requirements of public transportation, but also can participate in peak load shifting of a power grid, efficient utilization of energy and maximization of economic benefits are achieved, and powerful technical support is provided for sustainable development of the electric bus.

The invention discloses a trunk line intelligent network connection bus speed and signal timing collaborative optimization method, which comprises the following steps: acquiring basic information of bus lines, such as departure time interval, line length, station distribution and intersection signal phase; an intelligent networked bus speed control model is constructed, and dynamic speed guidance is implemented for different deviation types by calculating the deviation of the time headways of adjacent vehicles in real time; based on a vehicle speed control result, intersection signal lamp timing is optimized, and the negative influence of traffic signals on the driving speed of the public transport vehicle is reduced. By implementing accurate speed and signal lamp cooperative control at each bus station and intersection, the method can improve the overall operation efficiency of the trunk bus system to the maximum extent and reduce intersection delay while ensuring that the buses arrive on time, and compared with a traditional whole-line adjustment strategy, the method has the advantages that the overall operation efficiency of the trunk bus system is improved to the maximum extent, and the cost is reduced. According to the control strategy, the problem that the time headway deviation of a local road section is large can be solved more accurately.

The invention discloses a public traffic composite traffic network key node identification method based on an improved K-shell algorithm, and the method comprises the steps: firstly extracting the line, geographic position, vehicle arrival interval and rated passenger capacity information of a subway and a bus station from multi-source data, and constructing a weighted composite traffic network considering the intra-layer transportation capacity and inter-layer transfer relation; node stripping is carried out based on a K-shell algorithm, and a shell fine position KP value is calculated in combination with a node removal sequence; further introducing a neighborhood structure overlap ratio, and constructing a node local propagation influence index; meanwhile, a comprehensive correction coefficient is constructed by considering the difference between intra-layer node strength and transfer attributes and the transportation capacity difference between subway and bus layers; and finally, a node comprehensive importance index IKA is formed and is used for sorting and identifying network key nodes. Finally, the recognition effect is verified through network efficiency, connectivity and SIR (susceptibility-infection-recovery) propagation simulation, and the result shows that the method is superior to a traditional centrality algorithm in the aspects of recognition precision and propagation influence measurement.

The invention discloses a bus intelligent travel service dynamic recommendation transfer method, and relates to the technical field of bus services, and the method comprises the steps: S1, building a bus route set; s2, matching the overlapped stations with adjacent stations; s3, acquiring a starting point and an ending point of a user and generating candidate paths; s4, calculating transfer efficiency density, and performing path pruning; s5, calculating a departure time rhythm staggering coefficient between the transfer nodes; s6, calculating a transfer chain type risk accumulation function; s7, estimating total consumed time and waiting time; and S8, constructing a path comprehensive score and a recommendation sequence. According to the bus smart travel service dynamic recommendation transfer method provided by the invention, based on the public traffic operation path and station information, in combination with adjacent station distance calculation and departure rhythm feature construction, the structural association between transfer nodes can be identified more accurately, and path screening is realized through the rhythm interleaving factor, so that the transfer efficiency is improved. The recommendation frequency of the high-risk transfer path is effectively reduced, and the stability of the transfer process is improved.

A sensor bar or other array of sensors are mounted to the bus beneath or adjacent to a doorway of the bus. Ultrasonic sensors are located within the sensor bar and detect the presence of foreign objects such as an entrapped passenger close to the doorway. A safety output is generated by the system when an unsafe condition is detected. Examples of safety outputs can include warning sounds, warning lights, automatic door opening, using the brake transmission shift interlock (BTSI) to prevent transmission shifting for vehicle movement, and other safety outputs.

The bus shelter comprises a supporting assembly and a ceiling, a groove is formed in the bottom of the ceiling, a rack is fixedly arranged in the groove, and a first sliding groove and a second sliding groove are formed in the two sides of the groove respectively; a motor is fixedly installed in the first sliding groove, a lead screw is fixedly arranged on an output shaft of the motor and is in threaded connection with a second sliding block, and a rotating shaft is rotationally arranged on one side of the second sliding block. A sliding rod is fixedly arranged in the second sliding groove and slidably sleeved with a first sliding block, and the axis of the sliding rod is parallel to the axis of the lead screw. A gear and a baffle are fixedly connected to the rotating shaft. The gear is meshed with the rack. The rotating shaft is rotationally connected with the first sliding block. In the process that the motor drives the baffle to slide to the end of the ceiling, the gear makes contact with the rack, and therefore the baffle is driven to rotate. The baffle rotates to the inclined state so that the rain shielding area of the ceiling can be increased, meanwhile, rainwater can be prevented from being blown to the position below the ceiling, and passengers below the ceiling are prevented from being drenched by the rain.

The invention relates to the field of urban public transport deployment, and provides a public transport network matching method and system based on an urban space structure, which divides urban areas by constructing a traffic model, identifies areas with dense demands on the basis of power consumption density, and provides a public transport network matching method and system based on the urban space structure. And the coverage condition of the existing public transport network on the areas with dense demands is evaluated, and uncovered areas are identified. For the uncovered areas, the information of stores and companies in the map data is used to subdivide the uncovered areas into a plurality of types of areas. A bus rapid transit system is additionally arranged in a commercial district to improve transportation efficiency, a peak period special line is additionally arranged in an office building area to meet commuting requirements, a customized bus special line is provided in an industrial district to meet requirements of specific periods and routes, and a conventional bus route is additionally arranged in a residential district to improve daily travel. Accurate matching between the urban space structure and the public transport demand is realized, and the coverage rate and efficiency of public transport service are improved.

The invention provides a public transport line passenger flow prediction method for optimizing a deep neural network based on a CPO algorithm, and belongs to the technical field of public transport. Comprising the following steps: data collection and arrangement; obtaining multi-source data and preprocessing the data; mining features in the public transportation system; features in the public transportation system are integrated into the passenger flow prediction model; the training set is used to train the obtained CNN-LSTM-Attention model; calculating a fitness function of a CPO optimization layer through the verification set, outputting a performance evaluation and passenger flow prediction result, and obtaining a verified CNN-LSTM-Attention model; and the verified CNN-LSTM-Attention model is used to predict the passenger flow of the bus route. According to the method, multi-factor comprehensive consideration is effectively realized, and various factors influencing the public transport passenger flow are effectively fused. And through the strong feature learning ability of the deep neural network, potential relationships between the factors and the passenger flow are mined, so that an internal driving mechanism of passenger flow change is comprehensively and meticulously reflected, and a prediction result is more suitable for an actual operation scene.

The invention discloses a bus passenger getting-on station estimation method based on multi-source data fusion, and relates to the field of public traffic operation planning and management, and the method comprises the steps: obtaining multi-source sample data, and carrying out the integration processing, and obtaining database storage information; processing and extracting by using the public transport data preprocessing terminal to obtain a public transport operation time parameter; segmenting and constructing a fixed threshold time window; matching the intelligent card data to obtain an intelligent card data set of the candidate site; performing first data screening, constructing a candidate site group, and calculating by using a maximum likelihood estimation model to obtain a first data set of a determined site; performing second data screening and division to obtain a time sequence subset, and performing prediction by using a prediction neighbor information algorithm to obtain site information of the second data; and carrying out unified site numbering to obtain a site matching data set, and carrying out verification by using a multi-dimensional data terminal to obtain an evaluation result of the multi-dimensional data. According to the method, key field dependence is broken through, and data adaptability is improved.

The invention provides a conventional bus resource optimal configuration method and system considering subway toughness improvement, and belongs to the technical field of urban rail transit emergency connection. According to the method, actual factors such as a rail transit and bus network structure, conventional bus transportation requirements, metro emergency connection requirements, limited bus transportation capacity and bus system extension cost are comprehensively considered, and conventional bus service planning and metro interruption emergency response are collaboratively optimized; according to the method, stable operation of conventional buses can be guaranteed, meanwhile, passengers detained in the metro can be evacuated to the maximum extent, the service quality of a rail transit and bus connection mode can be improved, and the public transport network toughness can also be enhanced to the great extent. Meanwhile, in the collaborative optimization process of the conventional bus service and the bus connection service, a Pareto optimal method is introduced in view of the complexity of a dual-objective planning problem, so that efficient solving of an actual scale case is realized, and the method is closer to an actual problem.

The invention discloses an intelligent public transportation transfer recommendation system based on multi-source data fusion, and relates to the technical field of intelligent transportation systems, and the system comprises a transfer recommendation management center which is in communication connection with the following modules: a data acquisition module which is used for collecting and preprocessing traffic data from a plurality of traffic data sources, and transmitting the traffic data to a data processing module; various traffic modes of metros and buses are covered; and the multi-source data fusion analysis module is used for carrying out fusion processing on the collected multi-source heterogeneous data. By fusing multi-source traffic data and combining simulation analysis and real-time traffic states, the transfer path is dynamically optimized, the transfer waiting time and total consumed time of the user are remarkably shortened, the user can obtain optimal path recommendation based on real-time traffic information, long-time waiting caused by sudden delay is avoided, the travel efficiency and experience are improved, and meanwhile, the user experience is improved. Double constraints of walking distance and transfer waiting time are supported, it is ensured that path planning meets actual requirements of users, and travel experience is further optimized.

The invention relates to the field of intelligent traffic systems, in particular to a multi-source data fusion-based vehicle-road collaborative bus priority system, which comprises the following modules: a data acquisition module, which comprises an electronic police, a bayonet device, a monitoring device, a thunder-vision all-in-one machine and a vehicle-road system device and is used for acquiring traffic flow data at an intersection and related road sections thereof; the data processing module comprises a data cleaning unit, a data classification unit and a data analysis unit and is used for cleaning, classifying and analyzing the collected data and extracting traffic flow features, and the data fusion module is used for fusing the traffic flow features through three-level fusion (vehicle data fusion, traffic flow feature fusion and vehicle state fusion). Accurate vehicle position and state information and real-time traffic conditions of the intersection and the vicinity of the intersection are obtained, and the traffic prediction module can accurately predict the traffic flow condition of the whole intersection within a short period of time by combining real-time data and historical data.

The invention discloses an urban circle traffic demand prediction method and system, and belongs to the technical field of traffic data processing, and the method comprises the steps: taking a four-stage theory as a basis, efficiently obtaining a travel demand OD matrix of urban circle global traffic middle area granularity in the future, and mastering the overall traffic demand pattern of an urban circle from a macroscopic level; on the basis of a trip chain theory, a city traffic cell granularity trip demand OD matrix with higher time-space precision in the future year is accurately obtained, and the method can adapt to more application scenes and reflect traffic prediction and evaluation analysis of traffic demand details; through a traffic distribution algorithm of motor vehicles and public traffic, traffic flow indexes borne by urban circle traffic supply facilities are obtained, and continuity and consistency of spatial and temporal distribution of the current situation and indexes such as traffic supply facilities, travel demands, road flow and bus passenger volume in the future are achieved. And therefore, the traffic demand prediction and evaluation index result of the urban circle in the future year can scientifically meet the needs of users.

The invention provides a bus automatic driving method and system, and relates to the technical field of intelligent public transportation, passenger flow prediction is carried out on a target area, route planning is carried out according to a passenger flow prediction result, and a bus operation baseline is constructed; collecting travel reservation requests of users, performing spatial-temporal clustering analysis, and constructing a travel demand cluster; and performing scheduling decision analysis on the travel demand cluster and the candidate vehicle information, establishing a bus scheduling strategy, issuing and executing the strategy, receiving a real-time travel request of a user, performing matching analysis on the real-time travel request and the initial running path, and performing dynamic insertion updating on the initial running path according to an analysis result. The technical problems of low scheduling efficiency and resource waste caused by the fact that personalized and decentralized travel requirements cannot be responded in time due to fixed lines and scheduling response lag in the prior art are solved, and the technical effects of improving the bus travel service matching rate and enhancing the operation flexibility and the resource configuration efficiency are achieved.

The invention belongs to the technical field of urban traffic management, and particularly relates to an electric bus cooperative scheduling method in a charging facility time-sharing sharing mode. The objective of the invention is to solve the problem that the existing method faces a single electric bus route, neglects the fluctuation of the charging demand of private cars in each time period, and is easy to cause low utilization rate caused by excessive charging facility distribution number in individual time periods, or long charging queuing time of private cars caused by insufficient charging facility distribution number. The method comprises the following steps: constructing a collaborative optimization model of a bus scheduling scheme and a charging facility sharing scheme based on the daily charging cost of an electric bus line and an expression of an estimated value of earnings obtained by sharing a charging pile to an electric car every day by a bus enterprise; solving the collaborative optimization model to obtain an optimal feasible solution and a corresponding objective function value; the optimal feasible solution comprises an electric bus scheduling scheme and a charging facility sharing scheme.

The invention discloses a public transportation auxiliary unmanned aerial vehicle cooperative distribution method considering client point dynamic demands, introduces a public transportation auxiliary unmanned aerial vehicle with large service range coverage to carry out express distribution, and aims to reduce the terminal logistics distribution cost and improve the distribution timeliness. The method comprises the following steps: constructing a generation model which simulates a dynamic customer demand and considers a time window in combination with an actual scene, and establishing a bus auxiliary unmanned aerial vehicle cooperative distribution optimization model which considers the customer point dynamic demand based on a customer point generation mechanism and a bus unmanned aerial vehicle cooperative operation mechanism in the generation model; and aiming at the established model, designing a dynamic information processing strategy based on bus departure time, and fusing the dynamic information processing strategy into an adaptive large neighborhood search framework for solving, thereby obtaining a heuristic algorithm suitable for solving a bus-assisted unmanned aerial vehicle cooperative distribution problem considering the dynamic demand of a client point. The distribution path can be dynamically planned according to the demand points generated at any time, the logistics delivery efficiency is improved, and the distribution cost is reduced.

The invention discloses an intelligent bus scheduling system and method based on an AI large model, and the system comprises a data collection unit which is used for collecting multi-source urban traffic data; the data preprocessing unit is used for performing cleaning, standardization and feature extraction on the acquired multi-source urban traffic data; the knowledge graph construction unit is used for constructing the processed data into a knowledge graph containing entities, relationships and attributes; the AI reasoning unit is used for generating a natural language scheduling suggestion based on the knowledge graph state; the strategy optimization unit is used for optimizing a scheduling strategy based on a reinforcement learning algorithm; and the execution feedback unit is used for executing the scheduling instruction and collecting execution effect feedback. The problems that an existing bus dispatching system is insufficient in data utilization and lacks deep understanding and reasoning ability for a complex traffic environment are solved.

The invention provides a parallel main cabinet type direct current charging pile system, which comprises a main control unit, at least one power distribution matrix unit, at least one main cabinet and a plurality of charging terminals, and is characterized in that each main cabinet is internally provided with a plurality of pluggable power supply modules, and each charging terminal is provided with at least one charging gun; the switching control logic of the power distribution matrix unit supports any power scheduling across a main cabinet, across a charging terminal and across a charging gun; each power distribution matrix unit is constructed as an M * N full-connection switch matrix, and the power distribution matrix unit dynamically switches the output power of any one power supply module to any one charging gun. The main control unit dynamically dispatches the output ports of the SCU to be connected in parallel according to the number of the charging gun, automatic superposition of power of multiple main cabinets is achieved, the megawatt charging requirements of electric heavy trucks, buses and the like are met, any power module is supported to be correspondingly connected with the charging gun of any number, and the physical boundary among the main cabinets, the charging terminals and the charging gun is broken.

The invention discloses a traffic tool side window advertisement display management system and method, particularly relates to the field of intelligent bus advertisement systems, is used for solving the problems of accurate matching and seamless display of advertisements in a dynamic driving situation, and is used for accurately extracting vehicle driving state data through a multi-source data fusion and Kalman filtering technology. And carrying out feature analysis by using a deep learning model, and generating a driving situation label in real time. Based on the labels, the system intelligently screens out advertisements highly matched with the current situation, analyzes duration and robustness in combination with historical driving data, and optimizes an advertisement selection strategy by using a reinforcement learning algorithm. And finally, through a pre-caching mechanism and a seamless switching algorithm, it is ensured that the optimal advertisement content can be dynamically loaded and stably displayed on a bus side window display, and continuity and fluency of advertisement playing are achieved. The correlation and efficiency of advertisement putting are remarkably improved, and the riding experience of passengers and the advertisement communication effect are enhanced.

The invention discloses a subway interval interruption scene-oriented planned operation bus route fine adjustment method, which mainly comprises the following steps of: constructing a candidate bus route set and a rail station set capable of being conveniently transferred, and generating a multi-point connection path; on the premise of restraining the total demand of the detained passengers, the number of passengers getting on and off each bus station, the dynamic passenger capacity in the bus, the bus route arrival and departure time and the detour connection time are calculated, and the conventional operation service level is guaranteed; and constructing an optimization model aiming at minimizing the total delay of all system passengers, and solving the optimization model. By scientifically deploying existing operation bus resources, a multi-station dispersed transportation strategy is implemented, and travel requirements of passengers are accurately matched. The method effectively avoids the problem of congestion caused by centralized transportation of a single destination, and the constructed optimization model gives consideration to the operation efficiency of the public transportation system and the evacuation demands of detained passengers in a subway, remarkably reduces the overall delay cost, greatly improves the emergency response speed and the toughness of the public transportation system, and powerfully supports the safe and stable operation of an urban traffic system.

A peripheral device includes a bus interface and an Address Translation Service (ATS) controller. The bus interface is to communicate over a peripheral bus. The ATS controller is to communicate over the peripheral bus, including sending address translation requests and receiving address translations in response to the address translation requests, to cache at least some of the address translations in one or more Address Translation Caches (ATCs), to estimate one or more statistical properties of the received address translations, and to configure the one or more ATCs based on the one or more statistical properties.

The embodiment of the invention discloses a bus scheduling method and device based on the battery health state, and the method comprises the steps: obtaining the battery health state parameter, the battery remaining capacity, the real-time position information and the line information of each bus in real time, carrying out the loss coefficient evaluation of each bus line based on the line information, and obtaining the loss coefficient of each bus line; matching each loss coefficient obtained by evaluation with the battery health state parameter of each bus, and determining a plurality of dispatchable vehicles on each bus route; classifying the plurality of dispatchable vehicles according to the battery remaining capacity and the battery health state parameter of each dispatchable vehicle and the route information of the corresponding bus route, determining running vehicles and alternative vehicles on each bus route, and when the battery remaining capacity of the running vehicles is smaller than a preset capacity threshold value, sending the alternative vehicles to the dispatchable vehicles; and scheduling the alternative vehicles based on the real-time position information of the running vehicles. The matching precision of each bus route and the schedulable vehicle is improved, and the rationality and timeliness of vehicle scheduling are fully guaranteed.

The invention discloses a multi-model mutual learning public transport automatic scheduling method, and relates to the technical field of public transport, and the method comprises the steps: obtaining the real-time arrival, departure and station dwell time of each bus at each station in each line and the historical arrival, departure and station dwell time; acquiring real-time passenger flow and historical passenger flow of the station; on the basis of the collected data, all scheduling events occurring in actual operation are analyzed in real time, and the scheduling events comprise a vehicle fault event, a large passenger flow event and a vehicle delay event; generating a corresponding scheduling scheme for each scheduling event; and the dispatcher makes a final dispatching decision according to each dispatching event and the corresponding dispatching scheme. According to the method, analysis models and processing models of different scheduling events are established, characteristics and results of different models are integrated, the most suitable model is dynamically selected according to real-time scheduling requirements, simulation prediction and scheduling decision making are achieved, and finally intelligent and automatic scheduling of buses is achieved.

The invention provides an unmanned aerial vehicle terminal distribution method and system based on a public transport network, and belongs to the technical field of logistics distribution, and the method comprises the steps: obtaining customer information, public transport network information and unmanned aerial vehicle information; establishing a multi-target optimization model for cooperative distribution of the bus and the unmanned aerial vehicle by using the customer information, the public transportation network information and the unmanned aerial vehicle information; the target optimization module takes the minimum cost as a target function and considers time difference constraint, route constraint, load constraint and energy constraint; adopting a non-dominated transformer neighborhood search algorithm of a simulated annealing algorithm acceptance criterion to solve the multi-objective optimization model to obtain an approximate Pareto optimal solution set; the optimal solution set is an unmanned aerial vehicle tail end distribution scheme considering cost, time and risk. Based on the method, the invention further provides an unmanned aerial vehicle tail end distribution system based on the public transport network. According to the invention, through combination of unmanned aerial vehicle electric driving and public transportation network cooperation, multi-objective optimized logistics distribution is realized.

The invention provides a highway section real-time traffic volume calculation method and device based on an ETC portal, and the method comprises the steps: obtaining the road section basic data of a to-be-calculated highway road network and ETC portal basic data, and determining the basic information of an ETC cross modulation station according to the road section basic data and the ETC portal basic data; according to the basic information, acquiring first traffic volume data of toll vehicle models of the ETC cross-dispatching station in real time; converting the first traffic volume data into second traffic volume data of a cross-modulation standard vehicle type based on a preset division standard; and carrying out statistics on the second traffic volume data, and determining the traffic volume of each cross-modulation standard vehicle type of the ETC cross-modulation station. The comprehensiveness and accuracy of real-time operation monitoring of the highway network can be realized without additionally arranging other equipment for a newly built cross modulation station, the cost can be reduced, and meanwhile, powerful data support is provided for evaluating the traffic accessibility of the whole bus road network.

The invention provides an artificial intelligence-driven bus real-time scheduling and path optimization method. The artificial intelligence-driven bus real-time scheduling and path optimization method comprises the following steps: S1, multi-source data acquisition; s2, data preprocessing; s3, constructing an artificial intelligence scheduling optimization model; s4, generating a real-time scheduling decision; s5, vehicle execution and data feedback; and S6, path dynamic optimization. Through deep fusion of an artificial intelligence technology and public transportation services, the precision, dynamic and intelligent scheduling decision is realized, and finally the operation efficiency and service quality of a public transportation system are improved.

The invention relates to the technical field of intelligent transportation, in particular to a public transportation passenger and freight co-transportation scheduling system based on big data and the Internet of Things. Comprising data collection and processing, scheduling system architecture, path optimization and dynamic adjustment, and user interface and system management. Wherein the data collection and processing is used for the process of collecting and processing bus operation data by the system and is the basis of the system; the data collection and processing comprises passenger flow data, cargo flow data, vehicle state data and environment data; the scheduling system architecture is used for realizing comprehensive monitoring, real-time scheduling and resource optimization of bus passenger and freight co-transportation; according to the invention, by integrating real-time passenger flow data, cargo flow data and vehicle state information and utilizing big data analysis, an Internet of Things technology and an artificial intelligence algorithm, co-scheduling of buses and freight, path optimization and maximum utilization of resources are realized.

The utility model provides a passenger car body structure and a public bus, and belongs to the technical field of public buses, the passenger car body structure comprises a front wall framework, a rear wall framework, a left side coaming, a right side coaming, a top cover, a front wall skin, a rear wall upper skin and a rear wall headlamp skin. The left side coaming, the right side coaming and the top cover are all carbon fiber composite boards. The front wall skin, the rear wall upper skin and the rear wall headlamp skin are all carbon fiber composite boards. The steel skins fixed on the front wall framework and the rear wall framework are replaced by the carbon fiber composite plates, so that compared with carbon fiber composite materials only used in the left side coaming, the right side coaming and the top cover, the weight of the body of the passenger car or the coach can be further reduced, the strength and the rigidity of the body are not affected, and meanwhile, the weight of the passenger car or the coach can be reduced. And the lightweight degree of the whole vehicle can be further improved.