Difference between revisions of "Composite traffic"

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==Road data (Composite traffic model)==
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{{Var |
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Name        = Road data (Composite traffic model)|
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Focus      = Road data about connections between the defined areas in a city. (draft)|
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Scope      = At most one road connection between neighbouring areas. Exactly one route between any two areas. Within-area roads excluded. (draft)|
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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.
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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
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# are exclusive and mutually exhaustive
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# are as large as possible without creating very unrealistic routes between areas. Routes are defined in a way that between any two regions, there is only one specific road that is used to cross the region borders (and travel the distance between the regions if they are not neighbours).
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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.
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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.|
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Inputs      = Data tables
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*Routes outside
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*Routes inside
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*Roads
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*Area name |
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Definition  = |
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Unit        = |
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Result      = |
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Index      = |
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References  = [http://www.biomedcentral.com/1471-2458/5/123/ Tuomisto and Tainio: BMC Public Health 5:123, 2005]|
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}}
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==Distance data (Composite traffic model)==
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{{Var |
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Name        = Distance data (Composite traffic model)|
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Focus      = Lengths of routes and distances between areas. (draft)|
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Scope      = No alternative routes. (draft)|
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Description = |
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Inputs      = Data tables
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*In-area distance
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*Link length|
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Index      = Indexed by cities|
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Definition  = |
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Unit        = km|
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Result      = |
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References  = [http://www.biomedcentral.com/1471-2458/5/123/ Tuomisto and Tainio: BMC Public Health 5:123, 2005]|
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}}
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==Trip data (Composite traffic model)==
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{{Var |
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Name        = Trip data (Composite traffic model)|
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Focus      = Number of trips from one area to another area at a given time. (draft)|
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Scope      = One working day. Trips within the city area only. (draft)|
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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.
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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.
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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.|
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Inputs      = Data tables
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*Total trips
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*Population
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*Areal surface
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*HLT2004-2005 (Traffic study in Helsinki)
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*Workplaces
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*Inhabitants
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*Time in traffic
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*Trips per municipality
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*Trips per place
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*Trips per place and mode|
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Index      = |
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Definition  = |
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Unit        = #|
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Result      = |
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References  = [http://www.biomedcentral.com/1471-2458/5/123/ Tuomisto and Tainio: BMC Public Health 5:123, 2005]|
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}}
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==Trip aggregation (Composite traffic model)==
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{{Var |
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Name        = Trip aggregation (Composite traffic model)|
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Focus      = Aggregation of trips into composite vehicles, into buses, or into private cars. (draft)|
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Scope      = Composite vehicles have either 8 or 4 seats. (draft)|
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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.
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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).
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# Use an 8-seat vehicle if there are enough passengers to get it full.
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# Use a 4-seat vehicle if there are enough passengers to get it full. Divide the trips into two parts so that the passengers change vehicle in the most busy point along the route. Then,
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# Use an 8-seat vehicle if there are enough passengers to get it full.
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# Use a 4-seat vehicle if there are enough passengers to get it full.
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# Use a 4-seat vehicle for all remaining trips.
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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.
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The following outputs are calculated:
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*Number of passenger trips by mode (car or composite traffic)
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*Number of passenger trips by vehicle type. Note that in this output, the trip that includes a transfer is calculated twice.
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*Vehicle kilometres driven
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*Parking lots needed for the vehicles that are  used
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*Average vehicle numbers per hour for the 30 most busy links at 8.00-9.00 in the morning
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*Number of vehicles needed
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*Waiting time due to traffic jams and waiting for composite vehicle to arrive.
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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).|
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Inputs      = Data tables
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*Drop length (how many additional minutes does it take for a composite vehicle to stop once more to drop off passengers).
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*Drop points (number of drop points in each area).|
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Index      = |
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Definition  = |
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Unit        = # trips|
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Result      = |
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References  = [http://www.biomedcentral.com/1471-2458/5/123/ Tuomisto and Tainio: BMC Public Health 5:123, 2005]|
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}}
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==Costs (Composite traffic model)==
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{{Var |
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Name        = Costs (Composite traffic model)|
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Focus      = Different costs and pressures caused by the traffic activities. (draft)|
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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)|
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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).
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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.
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The following endpoints are considered (see Table 1):
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*Fraction of composite trips without change (%)
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*Vehicles needed (number)
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*Parking places need (number)
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*Average vehicle flow on the 30 most busy roads (vehicles/h at 8.00-9.00 AM)
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*Fine particle (<2.5 µm of diameter) emissions (kg per day)
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*Carbon dioxide emissions (ton per day)
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*Driver salaries (thousand e per day)
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*Vehicle capital and operational costs (thousand e per day)
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*Time cost (thousand e per day)
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*Average car trip cost to passenger (e per trip)
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*Expected composite trip cost to passenger (e per trip)
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The following costs are taken into account for passenger (P) or societal (S) costs:
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*Vehicle capital cost (P+S)
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*Driver salary cost (P+S)
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*Driving cost (fuel) (P+S)
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*Parking (parking fees for individual drivers) (P)
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*Parking land (opportunity cost of reserving land to parking purposes) (P+S)
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*Emissions (fine particles and carbon dioxide causing health and climate change effects, respectively (S)
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*Time for waiting composite vahicles, time spent in traffic jams (P+S)
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*Accidents (an option only, not used in the current model)
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*Ticket (profit for composite service provider) (P)
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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.
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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.|
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Inputs      = Data tables
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*Scenario data
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*Scenario description|
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Index      = |
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Definition  = |
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Unit        = € or €/trip|
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Result      = |
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References  = [http://www.biomedcentral.com/1471-2458/5/123/ Tuomisto and Tainio: BMC Public Health 5:123, 2005]|
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}}
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==VOI and importance analysis (Composite traffic model)==
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{{Var |
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Name        = VOI and importance analysis (Composite traffic model)|
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Focus      = Output tables and value-of-information analyses for different stakeholders. (draft)|
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Scope      = Stakeholders: A random passenger, society. (draft)|
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Description = Value of information analyses, studies on variation in the population, and other analyses on the results.|
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Inputs      = [[#Costs (Composite traffic model)]]|
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Index      = |
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Definition  = |
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Unit        = € or € per trip|
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Result      = |
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References  = [http://www.biomedcentral.com/1471-2458/5/123/ Tuomisto and Tainio: BMC Public Health 5:123, 2005]|
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}}
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==Cost elements (Composite traffic model)==
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{{Var |
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Name        = Cost elements (Composite traffic model)|
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Focus      = Input data for cost calculations. (draft)|
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Scope      = . (draft)|
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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.|
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Inputs      = Data tables:
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*Vehicle price
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*Fuel consumption
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*Fuel price
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*Car maintenance
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*Car occupancy
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*Driver salary
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*Vehicle lifetime
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*PM unit lethality
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*Emission unit cost
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*Parking space
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*Group size
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*Rush delay
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*Trips per car
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*Ticket
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*Time unit cost
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*Parking price
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*Accident costs
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*Accidents
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*Emission factor |
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Index      = |
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Definition  = |
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Unit        = various units, denending on input|
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Result      = |
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References  = [http://www.biomedcentral.com/1471-2458/5/123/ Tuomisto and Tainio: BMC Public Health 5:123, 2005]|
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}}
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===Trips per hour===
 
===Trips per hour===
  

Revision as of 12:44, 31 August 2006

Road data (Composite traffic model)

Road data (Composite traffic model)

Distance data (Composite traffic model)

Distance data (Composite traffic model)

Trip data (Composite traffic model)

Trip data (Composite traffic model)

Trip aggregation (Composite traffic model)

Trip aggregation (Composite traffic model)

Costs (Composite traffic model)

Costs (Composite traffic model)

VOI and importance analysis (Composite traffic model)

VOI and importance analysis (Composite traffic model)

Cost elements (Composite traffic model)

Cost elements (Composite traffic model)


Trips per hour

Trips per hour
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