NetworkSaturation: Key Indicator for Profitability and Sensitivity Analyses of PRT and GRT Systems
Abstract
1. Introduction
1.1. Background on PRT
1.2. State of the Art
1.3. Motivation and Objectives
2. Methods
2.1. Determination of Network Saturation and Maximum Network Saturation
- If a peak hour station-to-station demand matrix and the PRT network were available, then could be approximated by iteratively assigning the demand to the network, while increasing the demand level: (Step 1). Increase the demand by scaling up the demand matrix by a small factor, for example, 1.05. (Step 2). Assign the demand with the assignment method described in [26]. (Step 3). Verify if any of the link flows reached line-capacity limit; if not, go to (Step 1); if yes, calculate , where the minimum number of required vehicles can be determined from the link flows, link speeds and link travel times [26], e.g, the number of vehicles on link a equals , where is the flow, is the speed, and is the length of link a.
- Even if no station-to-station demand matrix is available, it is possible to estimate with simple heuristics: for example, if the length of trunk lines and the length of feeder lines were known, then the maximum network saturation could be estimated by ; this estimate simply assumes that the trunk lines operate at 90% and feeder lines at 10% capacity during peak hours.
2.2. Profit per Trip Analysis
3. Results
3.1. PRT with Single Vehicles
3.2. PRT with Platooned Vehicle
3.3. PRT/GRT Mixed Service
4. Discussion
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Case Study | Pop. Density [Inh./km2] | Guideway Length [km] | N° Veh. | [m/s] | Headway [s] | Net. Sat. S |
---|---|---|---|---|---|---|
Fornebu (Oslo) [16] | 2935 [17] | 7.8 | 285,300,610 | 12.5 | 2.5, 2.0, 1.0 | 1.14, 0.96, 0.98 |
Fornebu (Oslo) [18] | 2935 [17] | 16.0 | 200 | 10.0 | 3. | 0.40 |
Tyne And Wear (UK) [19] | 2087 [20] | 20.7 | 429 | 9. | 3. | 0.65 |
Cardiff (UK) [21] | 4637 [20] | 7.7 | 134–176 | 11.1 | 3.0 | 0.58–0.76 |
Corby (UK) [21] | 2613 [20] | 23.0 | 365 | 11.1 | 3.0 | 0.53 |
Corby (UK) [21] | 2613 [20] | 30.3 | 895–967 | 11.1 | 3.0 | 0.99–1.06 |
Heathrow (UK) [21] | 569 [22] | 7.6 | 78 | 11.1 | 3.0 | 0.34 |
Masdar City (UAE) [23] | 250 | 20.0 | 480–560 | 11.1 | 3.0 | 0.80–0.93 |
Rimini, (Italy) [24] | 1109 [20] | 18.0 | 425 | 11.1 | 3.0 | 0.71 |
Parameter | Value | Parameter | Value |
---|---|---|---|
Vehicle length [m] | 3.00 [21] | Available Places | 6 [27] |
Vehicle Mass [kg] | 850 [21] | Passenger Mass [kg] | 75 |
Line speed [m/s] | 11.11 [24] | Commercial speed [m/s] | 6.94 [24] |
Emergency Deceleration [m/s2] | 5.00 [28] | Brake React. Time [s] | 0.50 [24] |
Average Vehicle Occupation [pax] | 1.30 [27] | Average Trip Length [m] | 2500 |
Guideway Amort. Time [Years] | 40 | Station Amort. Time [Years] | 25 |
Vehicle Amort. Time [Years] | 25 | Interest Rate [%] | 4.00 |
Share Occupied Vehicles | 0.70 [27] | Ratio Trips 1 Peak Hour | 0.10 |
Peak Hours [hours/day] | 5.00 | Off-Peak Hours [hours/day] | 11.00 |
Demand Multiplier Peak | 1.00 | Demand Multiplier Off-Peak | 0.60 |
Station distance [m] | 1000 | Useful Life Time [Years] | 30 |
Component | Cost | Component | Cost |
---|---|---|---|
Guideway [EUR/m] | 5000 | Station [EUR] | 750,000 |
Vehicle [EUR] | 50,000 | Electricity cost [EUR/kWh] | 0.1899 |
Parameter | Small Vehicle | Large Vehicle | Increase |
---|---|---|---|
Number of Places | 6 | 12 | |
Vehicle Length [m] | 3 | 4.02 | |
Guidway Cost [EUR/m] | 5000 | 6300 | |
Station Cost [EUR] | 750,000 | 945,000 | |
Vehicle Cost [EUR] | 50,000 | 250,000 from [37] | |
Emergency Deceleration [m/s2] | 5.0 | 3.0 [28] | |
Carrying Capacity [] | 1914 | 1327 | |
Carrying Capacity [] | 11,483 | 15,919 | |
Vehicle Occupation off-peak | 1.3 | 1.3 | |
Vehicle Occupation peak | 3.9 | 7.8 | |
Carried Flow off-peak [] | 2488 | 2488 | |
Carried Flow peak [] | 7465 | 14,929 |
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Schweizer, J.; Bernieri, G.; Rupi, F. NetworkSaturation: Key Indicator for Profitability and Sensitivity Analyses of PRT and GRT Systems. Future Transp. 2025, 5, 104. https://doi.org/10.3390/futuretransp5030104
Schweizer J, Bernieri G, Rupi F. NetworkSaturation: Key Indicator for Profitability and Sensitivity Analyses of PRT and GRT Systems. Future Transportation. 2025; 5(3):104. https://doi.org/10.3390/futuretransp5030104
Chicago/Turabian StyleSchweizer, Joerg, Giacomo Bernieri, and Federico Rupi. 2025. "NetworkSaturation: Key Indicator for Profitability and Sensitivity Analyses of PRT and GRT Systems" Future Transportation 5, no. 3: 104. https://doi.org/10.3390/futuretransp5030104
APA StyleSchweizer, J., Bernieri, G., & Rupi, F. (2025). NetworkSaturation: Key Indicator for Profitability and Sensitivity Analyses of PRT and GRT Systems. Future Transportation, 5(3), 104. https://doi.org/10.3390/futuretransp5030104