LCT Paper

take vehicle population (according to type) as independet variable then by a sequence of relations calculate carbon emissions

1. Vehicle Number (N) = N_i(t)
2. yearly distance travelled (D) = N * VKT (average vechicle yearly distance travelled); VKT_i(a;t₀)
3. fuel consumption (F) = D * I (fuel intensity l/km) ; FI_i
4. emissions (E) = F * EC (Emission coefficients for fuels gm/l)

Critique:

1. current N can be obtained from data
   1. But the main data is vehicles issued so to find vehicles that are actually on road some assumptions regarding vehicle lifecyle must be done
   2. extrapolating this variable for future is again challenging.
      1. One way would be to extrapolate total vehicle count, and then apply modal share according to BAU or alternative scenarios
      2. Other way can be to extrapolate different vehicle population according to GDP, agriculture/economic production, population, … but this would cause problem to model the effects of alternative scenario (because alternative scenarios won't be affecting the independent GDP, populations,… variables)
2. VKT data is an rough estimations,
   1. VKT depends on vehicles age
   2. Also, to the change in attitude due to proliferation of public transport in alternative scenarios would change VKT but modeling the change in VKT might prove difficult (if not hopeless)
3. Fuel Consumption depends on terrain, vehicle age and other parameter but in my opinion we might as well take FI as constant
   1. Using FI also enables us to model improvements in technology and alternative fuel's effects.
4. Emission maybe taken constants in BAU and appropriate changes can be done for alternative scenarios

Feasibility:

1. modal share can be modelled
2. technological improvements in FI, EC can be modelled

take GDP and Population as independent variable

1. {I} = GDP (or GCP Gross City Product when analysing a single city) and Popultion (set of independent variables)
2. Travel demand = f ({I}) i.e. travel demand (passenger-km) is regressed with {I} and an equation is obtained
   • The past travel demand is calculated (obtained from other papers) using the procedure in CASE 1 (Bajracharja) . Travel demand_i (t) = Vechicle Count_i (t) * VKT_i * vehicle occupancy rate_i (t)
3. Travel demand is split into Road and Rail Demand.
4. Then modal share is used to calculate travel demand from specific mode
5. Then Vehicle demand (km) = Passenger-km demand of respective vehicle divided by vehilce occupancy
6. Fuel Consumption (F) = VD (km) * FI (l/km)
7. Emissions (E) = F * EC (Emission coefficients for fuels gm/l)

Critique:

1. Population might be independent but GDP depends on transport facilities. and the scenarios we adopt might lead to different GDP. However, the extend of this problem is unknown to me as it might be a philosophical/meta disagreement only I have. A possible counter idea might be to think that we check the feasibility/output of the scenarios when the GDP turns out (or we want/expect/prepare) to be what assumed.
   1. Future estimates of GDP and Population can be relaible take from other institutions as it is of concern to many organization and policy development.
2. This regression is not jusitfiable. Sure there must/is some correlation but all the varaiblity in transport demand is not addressed by {I}.
   1. However it seems more justifiable than to take vehicle registration trend.
3. [X] How exactly is it done? On what basis?
   • Finding : Nth important.
   • I don't think its necessary to split road and rail. Rail can be considered as a type of vehicle mode.
4. For BAU, older trend can be taken and for alternative scenarios, values that we target to achieve can be taken.
5. Average value might be difficult to find.
6. The effect of vehicle age is neglected. But can also be taken into consideration by monitoring changes in vehicle count (obtained from 5) each year and assigning newer FI to newer vehicles. (An eqn is given by IGES (nth special you can develop your own eqn))
7. Similar procedure to 6 can be adopted.
   1. (6) and (7) are good to consider changes in FI and EC from technological changes

Feasibility:

1. Modal share can be modelled
2. FI, EC can be modelled

1. Collect a set of independent parameters which affect Travel demand,
2. collect old data, then run a machine learning training, if the neural network doesn't seem to overfit the data, use it instead of the multilinear regression suggested/used by CASE 2 (IGES) in their paper.

• will require a parameter for urban sprawl in model
• If we are talking about KTM valley then its too late to think about TOD.
• [ ] The effects of TOD are difficult to model. (I think)
  • Because how exactly will TOD change travel demand or vehicle ownership? exactly can't be know.
  • [ ] However, the changes observed in other places can be used to somewhat okishly estimate changes in vehicle demand and ownership.

• Modal share estimate?

Compact Streets

(Cycling friendly streets) (i.e. Managing Multimodal transport)

[ ] (no term)

How to suggest integration of extra lanes.

• [ ] Feasibility in valley? Cost, trouble, hindrances.
• [ ] Feasibility in other cities.
• Bike lanes are not good enough (Youtube)
  • 43% of Dutch people bike daily!!!

[ ] (no term)

How to model their benefit?

• Read some paper you might get some idea.

• They are inevitable.
• You may also explore about car pooling (idk the exact term). In one interview Elon Musk agreed that people will own autonomous (self driving) electric cars and then when they are in work or when it's not need. They could earn from it by sending it to a pool of publicly usable vehilces (like taxi of sorts)
• Modelling the effect of EVs can be done by assuming a penetration ratio (i.e what % of people will choose EVs instead of older model vehicles)

• Ridesharing potential (i.e. no of travels from public vehicle that could potentially be done by ridesharing option) can be calculated and then the reduction of burden in public transport infrastructure may be estimated.
• [ ] Concerns
  • Adoption of this commute style. This doesn't seem to be major problem as people seem to have started liking existing ridesharing options.
  • [ ] Public Safety!! (Search about this)

• Sth that gives location of public buses.
• Makes booking of all kind of transport facilities convenient.
• Traffic Routing, …
• [X] How to measure its effect on carbon emission?
  • No way.
  • A possible way would be to review the effect of ITS measures in other countries and find (maybe) the increased adoption of public tranport. The resulting change in modal share can be used to estimate its effect.

• Is changes in emission standard necessary?
• What are the curretn emission standards in Nepal? Compare and anlayze wrt other countries/EU standard.