Resident trip mode comprehensive judging method based on handset signaling data

A travel mode, mobile phone signaling technology, applied in data processing applications, traffic control systems for road vehicles, traffic flow detection, etc. The effect of low cost and large information sample

Inactive Publication Date: 2015-12-02
SOUTHWEST JIAOTONG UNIV
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

The algorithm is complex, the amount of calculation is too large, and it is not easy to promote
[0011] 3. The identified travel modes are walking, bus, and car, which cannot be effectively identified for rail transit
[0019] 2. The recognized modes of travel are rail transit, ground public transport, private motor vehicles, bicycles and walking, but lack of identification of taxis and electric vehicles
[0027] 2. The identified modes of travel are walking and riding, and it is impossible to identify transportation methods such as rail transit
[0035] 2. The recognized travel modes are walking, public transportation, self-driving car / taxi, and other travel modes such as rail transit cannot be recognized

Method used

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  • Resident trip mode comprehensive judging method based on handset signaling data
  • Resident trip mode comprehensive judging method based on handset signaling data
  • Resident trip mode comprehensive judging method based on handset signaling data

Examples

Experimental program
Comparison scheme
Effect test

Embodiment

[0098] 1. Construct prior probability with the help of existing samples.

[0099] Table 4 Sample prior probability

[0100] Priori probability

value

p (walk)

0.6

p (bicycle)

0.4

p(average speedv2 | on foot)

0.8

p (average speed ≥ c v2 | on foot)

0.2

p(travel timet1 | on foot)

0.7

p(travel time ≥ c t1 | on foot)

0.3

p(travel distancel1 | on foot)

0.8

p(travel distance≥c l1 | on foot)

0.2

p(average speedv2 | bike)

0.3

p (average speed ≥ c v2 | bike)

0.7

p(travel timet1 | bike)

0.3

p(travel time ≥ c t1 | bike)

0.7

p(travel distancel1 | bike)

0.2

p(travel distance≥c l1 | bike)

0.8

[0101] 2. Obtain the mobile phone signaling data of a complete trip of the number 75 user, and select a single trip sub-chain.

[0102] Table 5 Mobile phone signaling data of the user's complete travel

[0103] ...

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PUM

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Abstract

The invention discloses a method of comprehensively judging a resident trip mode based on handset signaling data, which belongs to the transport planning and management data analyzing field. A data source is from handset signaling data provided by mobile network service providers. Data cleaning, integrating and position conversion are further performed. A resident trip mode is further judged by mobile space-time path describing and stopover point identifying. The method which can effectively discriminate seven common trip modes including walking, bicycling, routine bus, electric vehicle, self-driving, taxi and rail transit thus acquires trip mode information of residents. A data basis is further provided for fields of special traffic programs, comprehensive traffic programs and intelligent traffic systems of cities, etc.

Description

technical field [0001] The invention belongs to the field of traffic planning data analysis, and in particular relates to a method for comprehensively discriminating travel modes of residents based on mobile phone signaling data. Background technique [0002] Resident travel information plays a vital role in traffic planning, traffic control management, etc. It reveals the laws of urban land use, commercial activities, cultural customs, and bus network management, and is widely used in urban comprehensive traffic planning, Intelligent transportation systems and other fields. However, the existing travel surveys of residents are conducted in accordance with the questionnaire surveys and telephone inquiries approved by the American Society of Transportation Engineers, the European Transportation Association, my country's transportation management departments or industry associations. These traditional methods generally have high costs, heavy workloads, and data processing. Lon...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06Q10/04G08G1/01G08G1/015
Inventor 张锦任潜洋唐小虎陈以衡
Owner SOUTHWEST JIAOTONG UNIV
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