Taxi Demand Prediction Using Contingent Utilization Analysis
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Taxi drivers in New York City face challenges in finding passengers during non-peak hours due to incomplete knowledge of demand, leading to increased competition, traffic congestion, noise pollution, and fatigue, as well as limited passenger access to taxis.
Innovation Solution
A method that analyzes historical and recent taxi utilization data, combined with relevant conditions such as weather and event schedules, to predict current and future demand by generating representative information on where taxis are most likely needed, using a Contingent Taxi Utilization Analyzer Routine that filters and weights data to provide accurate projections.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If drivers search longer per fare to find passengers during non-peak hours, then passenger access to taxis is improved, but traffic congestion, noise pollution, and atmospheric pollution increase
Solution Approach 1:
The system performs preliminary analysis of historical taxi utilization data, weather conditions, and event schedules to predict future demand patterns. This advance preparation enables drivers to make informed decisions about where to position themselves before peak demand occurs, eliminating the need for prolonged searching and reducing unnecessary vehicle movement.
Solution Approach 2:
The system continuously analyzes historical data and provides feedback to drivers about predicted demand locations and times. This feedback loop allows drivers to adjust their positioning strategies based on predicted conditions, optimizing their search efficiency and reducing unnecessary driving in low-demand areas.
2Ease of operation
If drivers exert greater effort per fare to find passengers during non-peak hours, then passenger access to taxis is improved, but driver fatigue increases and road safety deteriorates
Solution Approach 1:
The system predicts demand patterns in advance and provides drivers with specific location and timing information before they need to search for passengers. This preliminary guidance reduces the intensity and duration of driver effort required during non-peak hours, thereby reducing fatigue and improving safety.
3Productivity
If drivers use incomplete knowledge about demand locations to search for passengers, then no additional infrastructure is needed, but search time per fare increases and efficiency decreases
Solution Approach 1:
The system replaces drivers' intuitive but incomplete knowledge of demand patterns with a data-driven predictive model. By substituting mechanical searching behavior with information-guided positioning, the system significantly reduces search time and improves overall taxi utilization efficiency.
Data Source
AI summary
Techniques are described for automatically analyzing contingencies in information predicting taxi demand. This is to generate representative information regarding current or future taxi demand, and for using such generated representative taxi demand. Contingent demand information may be generated for a variety of types of useful measures of taxi demand rates, such as for projecting expected likelihood of finding a passenger at each of several road locations. Generated representative contingent taxi demand information may be used in various ways to assist taxi and livery service drivers plan optimal routes and schedules. The historical and/or recent contingent demand data may be used to generate the representative traffic flow information. This may include data readings from mobile data sensors in the one or more vehicles, data sensors in or near the roads, or aggregate data sources collected from one or more sensors or through publicly available data sets.