Generalised cost is the sum of monetary and non-monetary cost of a journey. It is used for the purpose of judgement. Monetary cost includes public transport fare journey, fuel cost, wear and tear and any other parking charge.
Non-Monetary Cost involved time which is spent on undertaking the journey. Time is converted into monetary value figure using time value figure, which varies as per traveller income and trip purpose.
The generalised is equivalent to the goods price in demand and supply theory. It is seen that travellers are willing to spent time on some parts of journey rather than spent on others. The same is categorised into following:
- Walk from Origin
- Wait for the vehicle
- Ride in the vehicle
- Walk towards the destination
All travellers “dislike” all time spent in travelling, at the same time they dislike walking and also waiting parts of journey more than as compared to in-vehicle journey time, and are willing to pay for such more. This ultimately leads to higher time value for those journey parts than main in-vehicle journey part.
An alternative approach for the same is to apply weighting to time spent on each different part of journey which quantifies the level of dislike a traveller has for time spent on that bit of journey as compared to time spent on vehicles.
The model has 5 iterations
|
|
Attraction |
|
|
|||
Base |
|
H1 |
H2 |
S |
I |
Total P |
Target P |
Production |
H1 |
192 |
144 |
1123 |
524 |
1983 |
2382 |
H2 |
138 |
108 |
476 |
1021 |
1743 |
1986 |
|
S |
21 |
26 |
79 |
74 |
200 |
232 |
|
I |
25 |
24 |
11 |
16 |
76 |
84 |
|
|
Total A |
376 |
302 |
1689 |
1635 |
4002 |
|
|
Target A |
415 |
465 |
1940 |
1864 |
|
4684 |
|
Increase |
39 |
163 |
251 |
229 |
|
|
|
|
Attraction |
|
|
|
|||
1.P |
|
H1 |
H2 |
S |
I |
Total P |
Target P |
GF |
Production |
H1 |
230.6324 |
172.9743 |
1348.959 |
629.4342 |
2382 |
2382 |
1.201210287 |
H2 |
157.2392 |
123.0568 |
542.3614 |
1163.343 |
1986 |
1986 |
1.139414802 |
|
S |
24.36 |
30.16 |
91.64 |
85.84 |
232 |
232 |
1.16 |
|
I |
27.63158 |
26.52632 |
12.15789 |
17.68421 |
84 |
84 |
1.105263158 |
|
|
Total A |
439.8632 |
352.7174 |
1995.118 |
1896.301 |
4684 |
|
|
|
Target A |
415 |
465 |
1940 |
1864 |
|
4684 |
|
|
Increase |
24.8632 |
-112.283 |
55.11849 |
32.30091 |
|
|
|
|
Error |
5.99% |
-24.15% |
2.84% |
1.73% |
|
|
|
|
|
Too Big |
Too Big |
Too Big |
Too Big |
|
|
|
|
|
Attraction |
|
|
|
|
|
|||
2A |
|
H1 |
H2 |
S |
I |
Total P |
Target P |
Error |
|
Ratio |
Production |
H1 |
211.9149 |
221.7219 |
1289.888 |
597.392 |
2320.916 |
2382 |
2.56% |
Too Big |
1.026319 |
H2 |
152.3138 |
166.2914 |
546.7377 |
1164.002 |
2029.345 |
1986 |
-2.18% |
Too Big |
0.978641 |
|
S |
23.17819 |
40.03311 |
90.74008 |
84.36453 |
238.3159 |
232 |
-2.72% |
Too Big |
0.973498 |
|
I |
27.59309 |
36.95364 |
12.6347 |
18.24098 |
95.4224 |
84 |
-13.60% |
Too Big |
0.880296 |
|
|
Total A |
415 |
465 |
1940 |
1864 |
4684 |
|
|
|
|
|
Target A |
415 |
465 |
1940 |
1864 |
|
4684 |
|
|
|
|
Increase |
0 |
0 |
0 |
0 |
|
|
|
|
|
|
GF |
1.103723 |
1.539735 |
1.148609 |
1.140061 |
|
|
|
|
|
|
|
Attraction |
|
|
|
|
|||
A,P |
|
H1 |
H2 |
S |
I |
Total P |
Target P |
Increase Required |
Proportion |
Production |
H1 |
217.4922 |
227.5573 |
1323.836 |
613.1147 |
2382 |
2382 |
0 |
1 |
H2 |
149.0605 |
162.7395 |
535.0598 |
1139.14 |
1986 |
1986 |
0 |
1 |
|
S |
22.56392 |
38.97214 |
88.33526 |
82.12867 |
232 |
232 |
0 |
1 |
|
I |
24.29009 |
32.53016 |
11.12228 |
16.05747 |
84 |
84 |
0 |
1 |
|
|
Total A |
413.4068 |
461.7991 |
1958.353 |
1850.441 |
4684 |
|
|
|
|
Target A |
415 |
465 |
1940 |
1864 |
|
4684 |
|
|
|
Increase Required |
1.593239 |
3.200868 |
-18.3531 |
13.559 |
|
|
|
|
|
Error |
0.38% |
0.69% |
-0.95% |
0.73% |
|
|
|
|
|
Proportion |
1.003854 |
1.006931 |
0.990628 |
1.007327 |
|
|
|
|
|
|
Attraction |
|
|
|
|
|
|||
A,P |
|
H1 |
H2 |
S |
I |
Total P |
Target P |
Increase Required |
Proportion |
Error |
Production |
H1 |
218.3304 |
229.1346 |
1311.429 |
617.6072 |
2376.501 |
2382 |
5.498567848 |
1.002314 |
0.23% |
H2 |
149.635 |
163.8675 |
530.0454 |
1147.487 |
1991.035 |
1986 |
-5.035025552 |
0.997471 |
-0.25% |
|
S |
22.65088 |
39.24227 |
87.50741 |
82.73047 |
232.131 |
232 |
-0.131028367 |
0.999436 |
-0.06% |
|
I |
24.38371 |
32.75564 |
11.01804 |
16.17513 |
84.33251 |
84 |
-0.332513929 |
0.996057 |
-0.40% |
|
|
Total A |
415 |
465 |
1940 |
1864 |
4684 |
|
|
|
|
|
Target A |
415 |
465 |
1940 |
1864 |
|
4684 |
|
|
|
|
Increase Required |
0 |
0 |
0 |
0 |
|
|
|
|
|
|
Error |
0.00% |
0.00% |
0.00% |
0.00% |
|
|
|
|
|
|
Proportion |
1 |
1.539735 |
1.148609 |
1.140061 |
|
|
|
|
|
|
|
Attraction |
|
|
|
|
|
|||
A,P |
|
H1 |
H2 |
S |
I |
Total P |
Target P |
Increase Required |
Proportion |
Error |
Production |
H1 |
219 |
230 |
1314 |
619 |
2382 |
2382 |
0 |
1 |
0.00% |
H2 |
149 |
163 |
529 |
1145 |
1986 |
1986 |
0 |
1 |
0.00% |
|
S |
23 |
39 |
87 |
83 |
232 |
232 |
0 |
1 |
0.00% |
|
I |
24 |
33 |
11 |
16 |
84 |
84 |
0 |
1 |
0.00% |
|
|
Total A |
415 |
465 |
1942 |
1862 |
4684 |
|
|
|
|
|
Target A |
415 |
465 |
1940 |
1864 |
|
4684 |
|
|
|
|
Increase Required |
-0.01782 |
0.035545 |
-1.60104 |
1.583323 |
|
|
|
|
|
|
Error |
0.00% |
0.01% |
-0.08% |
0.09% |
|
|
|
|
|
|
Proportion |
0.999957 |
1.539735 |
1.148609 |
1.140061 |
|
|
|
|
|
|
|
Attraction |
|
|
|||
A,P |
|
H1 |
H2 |
S |
I |
Total P |
Target P |
Production |
H1 |
219 |
230 |
1318 |
620 |
2388 |
2382 |
H2 |
149 |
163 |
527 |
1142 |
1981 |
1986 |
|
S |
23 |
39 |
87 |
83 |
232 |
232 |
|
I |
24 |
32 |
11 |
16 |
84 |
84 |
|
|
Total A |
415 |
465 |
1943 |
1861 |
4684 |
|
|
Target A |
415 |
465 |
1940 |
1864 |
|
4684 |
|
|
Attraction |
|
|||
|
|
H1 |
H2 |
S |
I |
Total P |
Production |
H1 |
30 |
4 |
150 |
40 |
224 |
H2 |
39 |
17 |
51 |
106 |
213 |
|
S |
80 |
61 |
119 |
183 |
443 |
|
I |
60 |
111 |
335 |
271 |
777 |
|
|
Total A |
209 |
193 |
655 |
600 |
|
Total |
|
|
|
|
|
|
|
|
Attraction |
|
|||
|
|
H1 |
H2 |
S |
I |
Total P |
Production |
H1 |
249 |
234 |
1468 |
660 |
2612 |
H2 |
188 |
180 |
578 |
1248 |
2194 |
|
S |
103 |
100 |
206 |
266 |
675 |
|
I |
84 |
143 |
346 |
287 |
861 |
|
|
Total A |
624 |
658 |
2598 |
2461 |
|
|
|
|
|
e^(-0.2*c) |
Proportion flow |
Number |
Path |
O1-D1 |
18 |
|
0.027323722 |
24.64% |
42 |
|
01-02-03-D1 |
18 |
Model 1 |
0.027323722 |
24.64% |
42 |
|
01-D2-D3-D1 |
23 |
Model 1 |
0.010051836 |
9.07% |
15 |
|
01-02-03-D1 |
26 |
Model 2 |
0.005516564 |
4.98% |
8 |
|
01-02-03-D1 |
24 |
Model 3 |
0.008229747 |
7.42% |
13 |
|
O1-O3-D1 |
22 |
|
0.01227734 |
11.07% |
19 |
|
01-D2-D3-D1 |
24 |
Model 2 |
0.008229747 |
7.42% |
13 |
|
01-D2-D3-D1 |
28 |
Model 3 |
0.003697864 |
3.34% |
6 |
|
O1-D3-D1 |
24 |
|
0.008229747 |
7.42% |
13 |
|
Distance |
|
|
North Route |
7 |
|
|
Flow Vehicle Per hour |
Speed |
Time Taken |
Incremental Time |
0 |
50 |
8.4 |
0 |
100 |
50 |
8.4 |
8.4 |
200 |
50 |
8.4 |
0 |
300 |
42 |
10.00 |
1.6 |
400 |
34 |
12.4 |
2.4 |
500 |
26 |
16.2 |
3.8 |
600 |
18 |
23.3 |
7.2 |
700 |
10 |
42.0 |
18.7 |
800 |
10 |
42.0 |
0.0 |
900 |
10 |
42.0 |
0.0 |
1000 |
10 |
42.0 |
0.0 |
Increment |
Qa |
Ca |
Time Taken |
Incremental Time |
0 |
0 |
50.00 |
8.4 |
0 |
100 |
0 |
50.00 |
8.4 |
8.4 |
200 |
0 |
50.00 |
8.4 |
0 |
300 |
100 |
42 |
10.00 |
1.6 |
400 |
100 |
34 |
12.4 |
2.4 |
500 |
100 |
26 |
16.2 |
3.8 |
600 |
100 |
18 |
23.3 |
7.2 |
700 |
100 |
10 |
42.0 |
18.7 |
800 |
100 |
10 |
42.0 |
0.0 |
900 |
100 |
10 |
42.0 |
0.0 |
1000 |
100 |
10 |
42.0 |
0.0 |
North Route |
|
Slope |
-0.08 |
Intercept |
66 |
Va=50 |
if Qa=<200 |
Va=74- 0.08Qa |
if 200=<Qa=<700 |
Va=10 |
if Qa>=700 |
South Route |
|
Slope |
-0.09167 |
Intercept |
92.5 |
Va=65 |
if Qa=<300 |
Va=91.5- 0.09167Qa |
if 300=<Qa=<900 |
Va=10 |
if Qa>=900 |
At Equilibrium; |
|
Ca=Cb |
|
Ta=Tb |
|
Ta=7/Va |
|
Tb=6/Vb |
|
7/Va=6/Vb |
|
Vb=6/7Va |
|
Assuming that solution points lay on the slope of the speed-flow curves, substitute |
|
Va=74- 0.08Qa |
|
Vb=91.5- 0.09167Qb |
|
91.5-0.09167Qb=6/7*(74-0.08Qa) |
|
Qb=0.748Qa-300.78 |
|
Total flow = 1500; |
|
Qb=1500-Qa |
|
Qa = 1030.163 |
1030 |
Qb=470 |
470 |
North Route |
Distance |
|
|
|
7 |
|
|
Flow Vehicle Per hour |
Speed |
Time Taken |
Incremental Time |
0 |
50 |
8.4 |
0 |
100 |
50 |
8.4 |
8.4 |
200 |
50 |
8.4 |
0 |
300 |
42 |
10 |
1.6 |
400 |
34 |
12.4 |
2.4 |
500 |
26 |
16.2 |
3.8 |
600 |
18 |
23.3 |
7.2 |
700 |
10 |
42 |
18.7 |
800 |
10 |
42 |
0 |
900 |
10 |
42 |
0 |
1000 |
10 |
42 |
0 |
|
Distance |
|
|
South Route |
6 |
|
|
Flow Vehicle Per hour |
Speed |
Time Taken |
Incremental Time |
0 |
65 |
5.538462 |
0 |
100 |
65 |
5.54 |
5.5 |
200 |
65 |
5.54 |
0.0 |
300 |
65 |
5.54 |
0.0 |
400 |
55.83 |
6.45 |
0.9 |
500 |
46.67 |
7.71 |
1.3 |
600 |
37.50 |
9.60 |
1.9 |
700 |
28.33 |
12.71 |
3.1 |
800 |
19.17 |
18.78 |
6.1 |
900 |
10 |
36.00 |
17.2 |
1000 |
10 |
36.00 |
0.0 |
Equilibrium |
|
|
|
Combination |
|
|
|
North |
South |
Average time |
MC |
500 |
1000 |
29.38 |
|
600 |
900 |
30.93 |
|
700 |
800 |
29.62 |
|
800 |
700 |
28.33 |
Optimal Combination |
900 |
600 |
29.04 |
|
1000 |
500 |
30.57 |
|
Cost |
Small Electric Car |
Large Electric Car |
Small Diesel Car |
Large Diesel Car |
Purchase (P) |
25 |
35 |
14 |
23 |
Charger (c) |
2 |
2 |
0 |
0 |
Annual Maintenance (m) |
1 |
2 |
1 |
2 |
Weekly fuel (f) |
0 |
0 |
45 |
55 |
Weekly Electricity (e) |
20 |
30 |
0 |
0 |
Value |
-1.208 |
-1.848 |
-2.406 |
-3.042 |
Probability |
50.28% |
26.51% |
15.17% |
8.03% |
Electric |
||||
1 |
Small |
|||
Step 1 |
Logit Model |
Diesel |
||
Electric |
||||
2 |
Large |
|||
Diesel |
||||
Step 2: |
Consider the lower lever decision (Electric & Diesel) |
|||
Vf – Small Electric |
-1.208 |
|||
Vf- Small Diesel |
-2.406 |
|||
Pf – Small Electric |
76.82% |
|||
Pf- Small Diesel |
23.18% |
|||
Vf – Large Electric |
-1.848 |
|||
Vf- Large Diesel |
-3.042 |
|||
Pf – Large Electric |
76.75% |
|||
Pf- large Diesel |
23.25% |
|||
I Small |
-0.944254226 |
|||
V~small = |
0 |
|||
If lamda is 0.75 |
||||
VSmall = |
-0.70819067 |
|||
I Large |
-1.583325476 |
|||
V~Large = |
0 |
|||
If lamda is 0.75 |
||||
VSmall = |
-1.187494107 |
|||
Step 4: |
Consider the higher level decision (small or large) |
|||
elements common to utility are ignored |
||||
V Small = |
-0.70819 |
|||
V large = |
-1.18749 |
|||
P small = |
0.617583 |
|||
Plarge = |
0.382417 |
|||
Step 5 |
||||
Calculate final probability for each mode |
||||
Psmall electric = |
47.44% |
|||
Psmall diesel = |
14.32% |
|||
Plarge electric = |
29.35% |
|||
Plarge diesel = |
8.89% |
Microscopic modelling refers to break of process into small parts and then analyzing in a microscopic view. This modelling aims to reduce the turnaround time through efficient combination of activities which shall reduce air traffic delay or delay in construction of airport. This model helps to create scenario analysis through range of subgroups and selection of ideal combination. For instance, combination of cleaning activity in the plane.
Four stage demand modelling comprise 4 stages namely trip generation, trip distribution, modal split and assignment. The last first phase involves estimation of generalised cost and When assignment has been completed this will produce better estimates which can then be used to revise earlier predictions. Further, only when the whole model converges should results be produced. This model helps in better and efficient designing of the airport through cost minimization.
GIS Modelling helps to better understand the geographic area and helps to give the better understanding of the geography and topography of the area for the purpose of effective deign. It helps to create a 3D design model which helps to plan better.
A principal element which is missing from the 4-stage planning model is the time impact on transport planning. A ride-sharing request of travel can be created by people at any time through mobile app and be matched automatically after a few times. The waiting time of same can range from minutes to hours, it may also affect departure time of people, which is missing from four stage model.
Few literatures have considered departure time into account as a principal element when designing models or any schemes. It has been pointed out by Liu and Li that traffic congestion would change over a period of time and they designed the commuters. It has also been found out by Yin et al. that the effect of reducing of ride-sharing on UPV and traffic congestion was not same during morning and evening time, which also affect people choice of departure time. It is therefore departure time choice is important in transport planning, which ascertain the road condition and also the congestion level faced by travellers, which affect the traffic assignment.
Therefore, ride sharing effects the transport system in many forms as it becomes important to be considered as a new transport model.
Joint tours are a situation where either or both the activities and trips are same for different people involved.
If all the activities and trips are same between all the people than the same is a case of fully joint tour, but if only some of the trips or involved activities are same between different people involved than it is considered to be a partial joint tour.
Fully joint tour example: Husband and wife going together for a holiday vacation
Partial joint tour example: Husband and wife living together, wife going for shopping and husband goes to office and after they meet up again at the cinema afterwards.
User equilibrium is a method most widely used trip assignment method for auto trips. As per UE condition no driver has the opportunity to reduce the travel time by shifting to another route. It means travel time on each path will be same between any Origin-Destination.
Total system travel time can be found very much minimum under System Optimum condition. In this particular research, UE and SO methods for relevant assignments related to trip are compared.
The total system travel time can be found minimum in case of system Optimum condition. In this type of research, UE and SO method for trip assignments are made comparable. Firstly, the methodologies are compared, and then link flows and time of travel under both given conditions are make compared. As system Optimum is not a natural process. Arterial highway network of Sioux falls, South Dakota comprised of twenty-four nodes and seventy-six links with thirteen origins and destinations is also analysed for both UE and SO conditions. The analyses of network are done through GAMS optimization software. It is found that travel time is less under SO condition as compared to SE condition.