A method for predicting road traffic demand in large container ports

Abstract

The present invention discloses a method for predicting road traffic demand in a large container port area, comprising the following steps: data collection; calculating the probability distribution of port collection vehicles; calculating the probability distribution of port discharge vehicles; predicting the concentrated traffic volume of the container terminal. The present invention is based on the entry and exit rules of the collection and distribution vehicles of the container terminal, by analyzing the probability distribution of road traffic flow induced by the container terminal operation, and taking the law that the road traffic volume in the container port area changes with the terminal operation as the core, predicting the concentrated traffic volume in the container port area within the time period T. The present invention can reflect the impact of changes in the production and operation form of the container port on the road traffic in the container port area with high accuracy, and has high feasibility and effectiveness.

Description

Technical Field

[0001] The present invention relates to a container port road traffic management method, in particular to a large container port road traffic demand forecasting method Background Art

[0002] While serving the economy and promoting regional economic development, the large-scale port has also brought many problems to itself. One typical example is the road traffic problem within the container port. Solving this problem is of great significance for unblocking the bottleneck of the port transportation system and improving the efficiency of the logistics system headed by container port production. In order to solve the road traffic problem within the container port area, first of all, it is necessary to plan the road traffic system of the container port in advance according to the development status of the container port; secondly, it is necessary to implement effective management of the road traffic within the container port area. The above two levels of research are based on the demand form of road traffic within the container port area. Therefore, road traffic demand prediction is the key research content of road traffic within the container port area.

[0003] At present, there are few studies on the prediction of road traffic demand in port areas, and the existing studies basically follow the urban road traffic demand prediction model. However, urban road traffic demand is based on people's travel needs, while the road traffic demand in container port areas is induced by the production operations of the terminal. Therefore, the road traffic demand characteristics in container port areas are significantly different from those in urban areas. Specifically, in terms of road network, the road traffic in the container port area is simple and clear and only has motor vehicle lanes, while the urban road traffic has obvious network characteristics and many road types; in terms of traffic demand sources, the road traffic in the container port area is generated by cargo transportation, while the urban road traffic is generated by people's travel; in terms of traffic demand characteristics, the road traffic volume in the container port area is large, there is no fixed peak period, and the traffic flow characteristics of different cargoes are different, while the urban road traffic has a fixed peak period and timed orientation; in terms of travel route selection, the road traffic in the container port area has few options and basically no alternative routes, while the urban road traffic has many options; in terms of transportation tools, the road traffic in the container port area includes trucks, office vehicles and engineering vehicles, while the urban road traffic is diverse; in terms of traffic congestion, the road traffic in the container port area is easy to occur and lasts for a long time, while the urban road traffic will occur during the peak period and last for a short time. In summary, the road traffic in the container port area has the characteristics of a single entrance and exit, clear cargo categories, and heavy traffic volume, and cannot be simply predicted using the classic urban road traffic demand prediction model such as the "four-stage" prediction. Summary of the invention

[0004] The technical problem to be solved by the present invention is to provide a method for predicting road traffic demand in a large container port area so as to reasonably formulate a road traffic management plan in the container port area.

[0005] The technical solution adopted by the present invention to solve the above technical problems is:

[0006] A method for predicting road traffic demand in a large container port area comprises the following steps:

[0007] 1) Data Collection

[0008] Since container port operations are generally based on weeks, the number of consecutive weeks is set as the research time period T, and the time period length is expressed as d T , and the time point is represented as t = 1, 2…T. Then set up observation points to collect truck vehicle data and ship data. To collect vehicle data, set up observation points at the gates of each container terminal company to obtain the average single-vehicle container load w of container trucks C , the proportion of container cargo transported by road α C,J , the proportion of road transportation of containerized cargo α C,S In order to collect ship data, observation points are set up at the shore of each container terminal. The total number of arriving ships during the study period is N, and each container ship is represented by i=1,2…N. Then the export and import container volumes corresponding to container ship i are respectively expressed as and The start time and end time of the port collection operation are represented by T i J,S and T i J,E , forming the port collection operation time window [T i J,S ,T i J,E ], the starting time and ending time of port clearance operation are respectively represented as T i S,S and T i S,E , forming the port clearance operation time window [T i S,S ,T i S,E ].

[0009] 2) Calculate the probability distribution of vehicles entering the port

[0010] According to the statistical analysis of the observation points, the arrival time of container ships at the port is short and strict, and the induced road traffic flow shows a trend of high density and "more at the front and less at the back". The arrival of container trucks at the port follows the Bethe distribution. The probability distribution of vehicles arriving at the port is calculated using formula (1):

[0011]

[0012] In the above formula, q it represents the proportion of the container ship i's corresponding port vehicles arriving at time point t,

[0013] 3) Calculate the probability distribution of port vehicles

[0014] According to the statistical analysis of the observation points, the port clearance time of container ships is relatively long, and the induced road traffic flow shows a trend of low density and "high in the middle and low in the front and back". The port clearance arrival of container trucks follows the Weibull distribution. The probability distribution of port clearance vehicles is calculated using formula (2):

[0015]

[0016] In the above formula, p it represents the proportion of the port vehicles corresponding to container ship i arriving at time point t,

[0017] 4) Predicting the concentrated traffic volume at container terminals

[0018] The concentrated traffic volume of the container terminal is divided into two parts: port collection operation and port discharge operation. The concentrated traffic volume of the container terminal in time period T is calculated by formula (3):

[0019]

[0020] In the above formula, represents the concentrated traffic volume of the container terminal during the time period T; represents the port operation time window of container ship i [T i J,S ,T i J,E ] covers the time period T, where [T i J,S ,T i J,E ] Covering time period T Otherwise, 0; represents the port operation time window of container ship i [T i S,S ,T i S,E ] covers the time period T, where [T i S ,S ,T i S,E ] Covering time period T Otherwise 0.

[0021] Compared with the prior art, the advantages of the present invention are:

[0022] The present invention is based on the entry and exit rules of the collection and distribution vehicles of the container terminal, analyzes the probability distribution of road traffic flow induced by the container terminal operation, and takes the law that the road traffic volume in the container port area changes with the terminal operation as the core. A method for predicting road traffic demand in the port area of ​​a large container port is proposed. The method can reflect the impact of changes in the production and operation form of the container port on the road traffic in the container port area with high accuracy. The present invention has high feasibility and effectiveness. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] Figure 1 A schematic diagram of a location for collecting truck vehicle data and container ship data of the present invention;

[0024] Figure 2 It is a schematic diagram of the probability distribution of the container port traffic flow of the present invention;

[0025] Figure 3 It is a schematic diagram of the probability distribution of the container port traffic flow of the present invention. DETAILED DESCRIPTION

[0026] The technical solutions in the embodiments of the present invention will be described clearly and completely below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments.

[0027] Embodiment: A method for predicting road traffic demand in a large container port area comprises the following steps:

[0028] 1) Data Collection

[0029] Since container port operations are generally based on weeks, the number of consecutive weeks is set as the research time period T, and the time period length is expressed as d T , the time point is represented as t=1,2…T. Then set the observation points, whose spatial distribution is as follows Figure 1 As shown in Figure 1, we collect truck data and ship data. To collect vehicle data, we set up observation points at the gates of each container terminal company to obtain the average single-vehicle container load w of container trucks. C , the proportion of container cargo transported by road α C,J , the proportion of road transportation of containerized cargo α C,S In order to collect ship data, observation points are set up at the shore of each container terminal. The total number of arriving ships during the study period is N, and each container ship is represented by i=1,2…N. Then the export and import container volumes corresponding to container ship i are respectively expressed as and The start time and end time of the port collection operation are represented by T i J,S and T i J,E , forming the port collection operation time window [T i J,S ,T i J,E ], the starting time and ending time of port clearance operation are respectively represented as T i S,S and T i S,E , forming the port clearance operation time window [T i S,S ,T i S,E ].

[0030] 2) Calculate the probability distribution of vehicles entering the port

[0031] According to the statistical analysis of the observation points, the arrival time of container ships at ports is short and strict, and the induced road traffic flow shows a trend of high density and "more at the front and less at the back". The arrival time of container trucks at ports follows the Bethe distribution, such as Figure 2 As shown, the probability distribution of vehicles entering the port is calculated using formula (1):

[0032]

[0033] In the above formula, q it represents the proportion of the container ship i's corresponding port vehicles arriving at time point t,

[0034] 3) Calculate the probability distribution of port vehicles

[0035] According to the statistical analysis of the observation points, the port clearance time of container ships is relatively long, and the induced road traffic flow shows a trend of low density and "high in the middle and low in the front and back". The port clearance arrival of container trucks follows the Weibull distribution, such as Figure 3 As shown in Figure 2, the probability distribution of port vehicles is calculated using formula (2):

[0036]

[0037] In the above formula, p it represents the proportion of the port vehicles corresponding to container ship i arriving at time point t,

[0038] 4) Predicting the concentrated traffic volume at container terminals

[0039] The concentrated traffic volume of the container terminal is divided into two parts: port collection operation and port discharge operation. The concentrated traffic volume of the container terminal in time period T is calculated by formula (3):

[0040]

[0041] In the above formula, represents the concentrated traffic volume of the container terminal during the time period T; represents the port operation time window of container ship i [T i J,S ,T i J,E ] covers the time period T, where [T i J,S ,T i J,E ] Covering time period T Otherwise, 0; represents the port operation time window of container ship i [T i S,S ,T i S,E ] covers the time period T, where [T i S ,S ,T i S,E ] Covering time period T Otherwise 0.

[0042] What has been described above are only preferred specific implementations of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field, within the technical scope disclosed by the present invention, who makes equivalent replacements or changes according to the technical scheme and inventive concept of the present invention, should be covered by the protection scope of the present invention.

Claims

1. A method for predicting road traffic demand in a large container port area. Features The following steps are involved: 1) Data collection; 2) Calculate the probability distribution of vehicles entering the port; 3) Calculate the probability distribution of vehicles leaving the port; 4) Predict concentrated traffic volume at container terminals; The specific steps of data collection are as follows: Since container port operations are generally based on weeks, the number of consecutive weeks is set as the research time period T, and the time period length is expressed as d T , the time point is represented as t = 1, 2…T; then set up observation points to collect container truck vehicle data and ship data; in order to collect vehicle data, set up observation points at the gates of each container terminal company to obtain the average single vehicle load w of container trucks C , the proportion of container cargo transported by road α C,J , the proportion of road transportation of containerized cargo α C,S ; To collect ship data, observation points are set up at the shore of each container terminal. The total number of arriving ships during the study period is N, and each container ship is represented by i=1,2…N. Then the export and import container volumes corresponding to container ship i are respectively expressed as and The start time and end time of the port collection operation are represented by T i J,S and T i J,E , forming the port collection operation time window [T i J,S ,T i J,E ], the starting time and ending time of port clearance operation are respectively represented as T i S,S and T i S,E , forming the port clearance operation time window [T i S,S ,T i S,E ]; The specific steps of calculating the probability distribution of vehicles entering the port are as follows: According to the statistical analysis of the observation points, the arrival time of container ships at the port is short and strict, and the induced road traffic flow shows a high density and a distribution trend of "more at the front and less at the back". The arrival of container trucks at the port follows the Bethe distribution. Formula (1) is used to calculate the probability distribution of vehicles arriving at the port: In the above formula, q it represents the proportion of the container ship i's corresponding port vehicles arriving at time point t, The specific steps of calculating the probability distribution of port vehicles are as follows: According to the statistical analysis of the observation points, the port clearance time of container ships is relatively long, and the induced road traffic flow shows a trend of low density and "high in the middle and low in the front and back". The port clearance arrival of container trucks follows the Weibull distribution. The probability distribution of port clearance vehicles is calculated using formula (2): In the above formula, p it represents the proportion of the port vehicles corresponding to container ship i arriving at time point t, The specific steps of predicting the concentrated traffic volume of the container terminal are as follows: The concentrated traffic volume of the container terminal is divided into two parts: port collection operation and port discharge operation. The concentrated traffic volume of the container terminal in time period T is calculated by formula (3): In the above formula, represents the concentrated traffic volume of the container terminal during the time period T; represents the port operation time window of container ship i [T i J,S ,T i J,E ] covers the time period T, where [T i J,S ,T i J,E ] Covering time period T Otherwise, 0; represents the port operation time window of container ship i [T i S,S ,T i S,E ] covers the time period T, where [T i S,S ,T i S ,E ] Covering time period T Otherwise 0.

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