Train Crossing Delay Prediction for Road Travel Time Routing

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Solution Overview

Problem

Existing systems fail to effectively predict and manage traffic disruptions caused by trains at crossings, particularly due to the lack of publicly available schedules for freight trains and the difficulty in aggregating commuter train schedules, leading to unpredictable travel times on road segments near train crossings.

Innovation Solution

A system utilizing predictive modeling to estimate travel times for road segments near train crossings by analyzing train movement patterns, GPS coordinates, and vehicle historical data, incorporating a neural network to calculate estimated travel times based on train presence and road segment characteristics.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Reliability

If train schedule information is aggregated from multiple sources, then traffic disruption prediction capability is improved, but data collection complexity and time consumption increase

Engineering Contradiction:

Improvetraffic disruption prediction accuracyVSAvoiddata collection system complexity

Core Design Contradiction:

ReliabilityVSDevice complexity

Solution Approach 1:

The system segments train data collection by separating commuter train schedule data (from public sources) from freight train movement data (from GPS tracking), allowing each data type to be collected and processed through appropriate methods, thereby reducing overall system complexity while maintaining comprehensive coverage

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The system introduces an intermediary data processing layer that aggregates train schedule information from multiple public sources and correlates it with GPS tracking data, serving as a mediator between raw data sources and the prediction algorithm, thus improving prediction reliability without directly increasing collection complexity

Inventive Principle:

Principle #24Intermediary (Mediator)

2Measurement precision

If GPS tracking is used to monitor train positions in real-time, then traffic disruption detection accuracy is improved, but energy consumption and computational resources increase

Engineering Contradiction:

Improvetrain position detection accuracyVSAvoidGPS tracking energy consumption

Core Design Contradiction:

Measurement precisionVSUse of energy by moving object

Solution Approach 1:

The system implements periodic GPS position reporting at predetermined time intervals rather than continuous tracking, allowing sufficient precision for traffic disruption detection while significantly reducing energy consumption and computational processing requirements

Inventive Principle:

Principle #19Periodic action

Solution Approach 2:

The system dynamically adjusts the reporting interval parameter based on train speed and proximity to crossings, using shorter intervals when trains are approaching crossings (high risk period) and longer intervals during normal operation, optimizing the balance between detection accuracy and energy consumption

Inventive Principle:

Principle #35Parameter changes

3Adaptability or versatility

If multiple train crossing locations are monitored simultaneously, then overall traffic management coverage is improved, but system complexity and data processing requirements increase

Engineering Contradiction:

Improvegeographic coverage areaVSAvoidmulti-location monitoring system complexity

Core Design Contradiction:

Adaptability or versatilityVSDevice complexity

Solution Approach 1:

The system implements a universal data processing framework that handles multiple train crossing locations through standardized procedures and algorithms, allowing the same system architecture to scale across different geographic areas without proportionally increasing complexity

Inventive Principle:

Principle #6Universality (Multi-functionality)

4Measurement precision

If historical vehicle data is collected and analyzed, then travel time estimation accuracy is improved, but data storage requirements and processing time increase

Engineering Contradiction:

Improvetravel time estimation accuracyVSAvoidhistorical data volume

Core Design Contradiction:

Measurement precisionVSQuantity of substance

Solution Approach 1:

The system extracts and utilizes only the most relevant features from historical vehicle data (such as average travel times, delay patterns, and traffic flow characteristics) rather than storing and processing complete raw datasets, reducing data storage requirements while maintaining estimation accuracy

Inventive Principle:

Principle #2Taking out (Extraction)

Data Source

PatentUS12540827B2Traffic prediction and routing using train movement patterns

Publication Date: 2026.02.03 HERE GLOBAL BV

AI summary

A method and apparatus for calculating an estimated travel time and a method and apparatus for defining a model configured to estimate a travel time are provided. The method for estimating a travel time includes obtaining a movement pattern for a train, determining one or more predicted times when the train will be present at a train crossing where a road path and a train path intersect using the movement pattern, obtaining vehicle historical data including an average time for a vehicle to travel one or more road segments adjoining the train crossing, obtaining a start time, and calculating the travel time for the one or more road segments based on the vehicle historical data, the start time, and the predicted time when the train will be present at the train crossing.