Composite traffic
Composite traffic is a hypothetical traffic system based on large-scale demand-responsive public transportation.
Contents
See also
- Tuomisto and Tainio: An economic way of reducing health, environmental, and other pressures of urban traffic: a decision analysis on trip aggregation. BMC Public Health 2005; 5: 123
Old page that should be restructured
Composite traffic describes a decision analysis related to urban traffic and its health, environmental and other pressures.
Road data
| Focus: | Road data about connections between the defined areas in a city. (draft) |
| Scope: | At most one road connection between neighbouring areas. Exactly one route between any two areas. Within-area roads excluded. (draft) |
| Description: | This module creates the node Route matrix, which contains the driving instructions from all areas to all other areas. Distances calculates the distances (by road) between the areas.
To make the construction of Route matrix as simple as possible for a new city, the roads are defined in the following way. First, the whole metropolitan are is divided into 15 regions, and these regions are further divided into 129 areas with 7300 inhabitants on average. The 129 areas are standard areas for urban planning, but the regions were formed for this particular purpose. The criteria for forming a region were that they
It is thus necessary to describe the routes between all areas within each region, and the routes between all regions. However, then it is possible to deduce the detailed routes between two areas that are in different regions using these hierarchical instructions. The routes are described as lists of areas that are along the road between the origin and destination. The route description needs not be in full detail if the details between two areas are defined in Roads node. A minimum number of existing roads were selected so that the routes in the model would not be very unrealistic. This work was done manually with a map. Note that the absolute numbers of 'Average vehicle flow on the 30 most busy roads' are likely biased upwards because all traffic from smaller streets is packed to the major roads in the model. |
| Inputs: | Data tables
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| Index: | |
| Definition: | |
| Unit: | |
| Result: | |
| References: | Tuomisto and Tainio: BMC Public Health 5:123, 2005 |
Distance data
| Focus: | Lengths of routes and distances between areas. (draft) |
| Scope: | No alternative routes. (draft) |
| Description: | |
| Inputs: | Data tables
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| Index: | Indexed by cities |
| Definition: | |
| Unit: | km |
| Result: | |
| References: | Tuomisto and Tainio: BMC Public Health 5:123, 2005 |
Trip data
| Focus: | Number of trips from one area to another area at a given time. (draft) |
| Scope: | One working day. Trips within the city area only. (draft) |
| Description: | This module calculates the trip rate for each origin-destination pair (129^2 pairs) and for each time point (12 min intervals resulting in 120 time points) based on trip data from three separate hours (morning rush, midday, afternoon rush) and time activity (based on diaries) in traffic along 24 hours.
The total number of trips equals the number of car trips in Helsinki area on a working day in 2000. All scenarios have the same street strucure and number of trips with a particular origin, destination, and time. The trips are divided into car trips and composite trips differently in each scenario based on two variables. Composite fraction is the percentage of the trips that are handled by composite traffic; the remaining trips are handled by personal cars. Guaranteed area defines the area where composite traffic is provided (i.e. the area where you are guaranteed to get a composite vehicle if you want one). The default assumption is that both the origin AND the destination must be in the guaranteed area, but it is also easy to evaluate scenarios where the guarantee covers all trips in the Helsinki area as long as either the origin OR the destination is in the guaranteed area. The model calculates the expected number of trips for each origin-destination-time cell, and picks one random number from Poisson distributioin based on the expectation. After that, the model is deterministic all the way to Outputs node. |
| Inputs: | Data tables
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| Index: | |
| Definition: | |
| Unit: | # |
| Result: | |
| References: | Tuomisto and Tainio: BMC Public Health 5:123, 2005 |
Trip aggregation
| Focus: | Aggregation of trips into composite vehicles, into buses, or into private cars. (draft) |
| Scope: | Composite vehicles have either 8 or 4 seats. (draft) |
| Description: | This module calculates the actual trips, modes of transportation, and delays during trips and vehicle transfers. It also calculates the kilometres traveled by each type of vehicle and number of vehicles needed.
The composite traffic trips are allocated into different vehicles. The following hierarchy is used in allocation. If the criterion is fulfilled, that number of passengers is allocated, and the rest will go to the next criterion. The criteria are used for a group of trips that has the same origin, destination, and time. Time resolution is 12 min. Origin and destination are described as '129-areas' used for city authorities in Helsinki metropolitan area. The 129 areas have on average 7300 inhabitants (0, 25%, 50%, 75%, and 100% percentiles are 0, 3400, 6800, 10300, and 28300, respectively).
The criterion is checked at the actual arrival time at the transfer point, i.e. the model takes into account the different travel times between areas. The following outputs are calculated:
The outputs of each scenario are indexed (when relevant) by period (day, evening, night); zone (Helsinki downtown, other centre, suburb), length of trip (less or more than 5 km), and vehicle type (8-seat or 4-seat vehicle with of without transfer, or car). |
| Inputs: | Data tables
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| Index: | |
| Definition: | |
| Unit: | # trips |
| Result: | |
| References: | Tuomisto and Tainio: BMC Public Health 5:123, 2005 |
Costs
| Focus: | Different costs and pressures caused by the traffic activities. (draft) |
| Scope: | Costs are limited to those that can be calculated from vehicle number, km driven, average waiting time, peak vehicle number per link, or parking places needed. Indirect costs are included. (draft) |
| Description: | This module calculates various pressures of different traffic scenarios. The estimates are based on Outputs node (which has been calculated beforehand due to slow calculations) and the numbers are stored in Static nodes). The outputs of each scenario are indexed (when relevant) by period (day, evening, night); zone (Helsinki downtown, other centre, suburb), length of trip (less or more than 5 km), and vehicle type (8-seat or 4-seat vehicle with of without transfer, or car).
Costs are separately calculated for the passenger and the society. Some costs affect these stakeholders differently, such as fine particle and carbon dioxide emissions: they are calculated as societal costs only, not as costs to a passenger. The following endpoints are considered (see Table 1):
The following costs are taken into account for passenger (P) or societal (S) costs:
The module has a submodule Cost elements. It contains the detailed descriptions of the unit costs and other input variables that are used to calculate the pressures of each scenario. The values used are dependent on the stakeholder. For example, the car price is the price that a random new car would cost, and it has therefore large uncertainty. On the other hand, the price of a 4-seat composite vehicle is the average price a taxi-style car would cost in Finland, and the confidence intervals are narrower because there is no individual uncertainty. This is because the price of an individual car affects the costs of individual car trips, while the cost of composite trip is dependent on the total cost of vehicles. Variation between individuals has been separately estimated for three variables: how passengers evaluate the capital costs of owning a car; how passengers are willing to pay for either the right to drive themselves or to not need to drive; and how many passengers are traveling together. |
| Inputs: | Data tables
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| Index: | |
| Definition: | |
| Unit: | € or €/trip |
| Result: | |
| References: | Tuomisto and Tainio: BMC Public Health 5:123, 2005 |
VOI and importance analysis
| Focus: | Output tables and value-of-information analyses for different stakeholders. (draft) |
| Scope: | Stakeholders: A random passenger, society. (draft) |
| Description: | Value of information analyses, studies on variation in the population, and other analyses on the results. |
| Inputs: | #Costs (Composite traffic model) |
| Index: | |
| Definition: | |
| Unit: | € or € per trip |
| Result: | |
| References: | Tuomisto and Tainio: BMC Public Health 5:123, 2005 |
Cost elements
| Focus: | Input data for cost calculations. (draft) |
| Scope: | . (draft) |
| Description: | This module contains the detailed descriptions of the unit costs and other input variables that are used to calculate the pressures of each scenario. The values used are dependent on the context. For example, the car price is the price that a random new car would cost, and it has therefore large uncertainty. On the other hand, the price of a 4-seat composite vehicle is the average price a taxi-style car would cost in Finland, and the confidence intervals are narrower because there is no individual uncertainty. This is because the price of an individual car affects the costs of individual car trips, while the cost of a composite trip is dependent on the total cost of vehicles to the service provider. |
| Inputs: | Data tables:
|
| Index: | |
| Definition: | |
| Unit: | various units, denending on input |
| Result: | |
| References: | Tuomisto and Tainio: BMC Public Health 5:123, 2005 |
Trips per hour
| Focus: | Total number of composite trips travelled per hour in the Helsinki Metropolitan Area. R↻ |
| Scope: | Trips of one working day, based on year 2000 data. Private car trips as the starting point; 50% of them were assumed to switch to composite traffic. D↷ |
| Description: | Variable 'Composite trip aggregation' was used to calculate, which type of vehicles, when, and where could be used when trips were aggregated into public vehicles. 'Trips per hour' is a simple summary of the more detailed result: location has been summed up. |
| Inputs: | #Composite trip aggregation |
| Index: | |
| Definition: | sum(sum(Composite_trip_aggregation,From),To1)/time_unit |
| Unit: | trips/h |
| Result: | |
| References: | Tuomisto and Tainio: BMC Public Health 5:123, 2005 |
