Public Transit Load Profile via AVL Dwell Time Analysis
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Solution Overview
Problem
Public transport systems face high costs and inefficiencies in determining passenger demand due to resource-intensive methods like automatic passenger counting and video surveillance, which are costly to deploy and maintain across entire networks.
Innovation Solution
A method using automatic vehicle location data to compute dwell times and load profiles, employing local regression procedures to represent load progression rates, and constraining these rates with historical data, thereby providing a typical load profile for vehicle routes with reduced costs and increased flexibility.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If automatic passenger counting systems with RFID readers and information systems are deployed, then measurement precision of passenger demand is improved, but device complexity and cost increase significantly
Solution Approach 1:
The invention extracts the essential function of passenger counting from complex APC systems and implements it using only existing AVL data. By taking out the core requirement (passenger demand measurement) and satisfying it through a simpler data source (AVL dwell times), the system avoids the complexity of RFID readers, APC software, and additional hardware infrastructure.
Solution Approach 2:
The invention makes the existing AVL system serve multiple purposes by extracting passenger demand information from its operational data. The AVL system, already deployed for vehicle tracking, now also provides passenger load profiles through analysis of dwell times and trip data, eliminating the need for separate counting systems.
2Measurement precision
If weight sensors are installed on all vehicles to collect passenger data, then measurement precision is improved, but device complexity and cost increase
Solution Approach 1:
The invention extracts passenger demand information from the operational parameters of existing AVL systems rather than requiring physical weight measurement. By analyzing dwell times and trip patterns from GPS data, the system derives load profiles without needing weight sensors on vehicles.
Solution Approach 2:
Instead of directly measuring passenger weight, the invention creates a computational model that copies the effect of weight measurement by inferring passenger numbers from dwell time patterns. The load profile computed from AVL data serves as a virtual copy of what weight sensors would measure, achieving the same informational goal without physical sensors.
3Measurement precision
If video cameras are installed at all stops for passenger counting, then measurement precision is improved, but device complexity and maintenance cost increase
Solution Approach 1:
The invention extracts passenger flow information from the movement patterns of vehicles captured by existing AVL GPS systems. By analyzing when vehicles stop and how long they remain stationary at each location, the system infers passenger boarding and alighting without needing visual observation cameras at stops.
Solution Approach 2:
The invention uses vehicle location data as an intermediary to indirectly measure passenger flow. Instead of directly observing passengers with cameras, the system uses GPS-tracked vehicle positions and dwell times as a mediator to compute load profiles, avoiding the need for direct visual counting infrastructure.
4Measurement precision
If multiple data collection systems are deployed across the network, then measurement precision is improved, but loss of energy and operational cost increase
Solution Approach 1:
The invention makes the AVL system universal by extracting multiple types of information from a single data source. The same GPS tracking infrastructure used for fleet management and operational control now also provides passenger demand data, eliminating the need for separate APC systems and reducing overall operational costs.
Solution Approach 2:
The AVL system serves itself by generating passenger demand information from its own operational data. The system analyzes its own trip and dwell time records to compute load profiles, making the data collection self-sufficient without requiring additional energy-intensive counting infrastructure.
Data Source
AI summary
A method for providing a typical load profile of a vehicle includes computing dwell times and maximum load stops, wherein a maximum load stop indicates a stop having a largest load on a route for a trip using route information and vehicle scheduling information based on automatic vehicle location data, computing one or more trip load profiles by identifying a load progression rate by performing a local regression procedure dividing the route into subsections such that the loads between adjacent subsections can be represented by a linear function on input of the computed maximum load, the computed dwell times, and maximum load stops, and constraining the identified load progression rate to an admissible value by evaluating the rate load progression with regard to historical dwell times and the computed maximum load stops. The method further includes computing the typical load profile based on load profiles of computed trips.