1 Introduction
2 Literature review
3 Taxi industry health degree and evaluation criteria
3.1 Taxi industry health degree
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Robustness: Companies within the industry are well-organized, operating in good condition. The amount of revenue can be estimated correctly, and almost every big oscillation can be predicted.
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Productivity: Companies within the industry are capable of maintaining a balance of demand and supply, and resources are utilized efficiently.
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Sustainability: Companies within the industry can reach a balanced budget at least, and the income of the drivers is guaranteed. Besides, both energy used and carbon should be kept to a reasonable level.
Degree | Very healthy | Healthy | Nearly healthy | Unhealthy | Very unhealthy |
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Value | 90–100 | 80–90 | 70–80 | 60–70 | 0–60 |
3.2 Evaluation indicator system for taxi industry health degree
Criteria | Reference |
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(1)Number, (2)Fare, (3)Disposable income, (4)Availability, (5)Occupied journey time, (6)Daily passenger demand, (7)Passenger and taxi waiting time, (8)Utilization | H. Yang et al. (2000) [3] |
(1)Passenger waiting time, (2)Taxi revenue, (3)Costs of operations | S. Li (2006) [47] |
(1)Driver income, (2)Company’s total revenue, (3)Taxi waiting time, (4)Driving speed, (5)Accident rate | He (2009) [48] |
(1)Number, (2)Mileage utilization, (3)Service availability, (4)Service availability for disabled people | Chen (2009) [1] |
(1)Revenue per mile, (2)Passenger satisfaction degree, (3)Mileage and time utilization, (4)Passenger number | Hassan et al. (2013) [46] |
(1)Number, (2)Mileage utilization, (3)Total travel mileage, (4)Companies’ total revenue, (5)Fuel consumption | Bai et al. (2014) [20] |
(1)Accessibility, (2)Regularity, (3)Reliability, (4)Convenience, (5)Effectiveness | Miteva, Pencheva, & Grozev (2015) [49] |
(1)On time, (2)Safety, (3)Crowd level, (4)Noise level, (5)Travel time, (6)Cleanliness | Shaaban & Kim (2016) [50] |
Industry manager | Company manager | Taxicab driver | Passenger | Academician | |
---|---|---|---|---|---|
Robustness | 5 | 4 | 3 | 2 | 5 |
Productivity | 5 | 5 | 5 | 4 | 5 |
Sustainability | 5 | 3 | 2 | 4 | 5 |
3.2.1 Robustness (A)
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Predictability (A1)
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A11:Order Volatility (OV): Order means taxi trip, including occupied and empty trips. TO t denotes the amount of taxi trips in t th period; s is the number of period; ATO is the mean of total amount of taxi trips of s periods.
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A12:Revenue Growth (RG), TR t denotes the sum of revenue during t th period.
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Survival rates (A2)
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A11:Order Amount (OA): The amount of valid taxi trips are calculated by using the amount of total taxi trips recorded by GPS minus the repetitive and wrong records. The repetitive records means more than one taxi trips running at the same time recorded by one GPS device. The wrong records are recorded taxi trips which are not consistent with real trips. TR denotes total data record; RR n is the nth repetitive record; WR m is the mth wrong record.
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A21:Practitioner (P), P is the total amount of employees in the taxi industry, including drivers and administrative workers. It can be obtained from Statistical Reports.
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A22: Operating Vehicles (V), V is the amount of vehicles that both GPS and Taximeters are of working status.
3.2.2 Productivity (B)
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Efficiency (B1)
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B11:Mileage utilization (MU): M i denotes the mileage of valid taxi trip i; TM denotes the overall mileage of the same period.
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B12:Time utilization (TU): AT i denotes the off-taxi time of valid taxi trip i; BT i denotes the on-taxi time of valid taxi trip i; TT denotes the overall time of the same period.
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Effect (B2)
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B21:Average operating time (AT): OT k denotes the operating time that k th taxicab spent during t th period; N is the amount of operating vehicles.
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B22:Average mileage (AM): OM k denotes the mileage that k th taxicab covered during t th period.
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B13:Average speed (AS): M i is the mileage of valid taxi trip i; AT i is the off-taxi time; BT i is the on-taxi time.
3.2.3 Sustainability (C)
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Economy (C1)
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C11:Total revenue (TR): RPO denotes the revenue of i th valid taxi trip; TO is the amount of total trips.
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C12:Average daily income (ADI): TR i represents the total income of period i; AN i is the average operating vehicles at the same period.
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C13:Income difference (ID): Ii is the income of taxi i; ADI is the average daily income, and N is the amount of vehicles.
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Environment (C2)
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C21: Energy intensity (EI): VN ij is the amount of vehicles; ATD ij is the average mileage; FE ij is the intensity of fuels (e.g. 0.725 kg/l for 93 gasoline); i is the kind of fuels; j is the vehicle’s age.
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C22: Carbon intensity (CI): EC i is the amount of fuel i consumption; EFi is the CO2 Emission Factor of fuel i. According to IPCC national greenhouse gas inventory Guide 2016, the CO2 Emission Factor of gasoline is 3.06556 kgCO2/kg.
4 Synthetic evaluation process of taxi industry health degree
4.1 Define the criteria set
4.2 Define the weights by AHP
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Step 1:Articulate preferences with matrix A in which element a ij (i, j = 1, 2, …, n) is the quotient of weights of the criteria. The value of a ij is determined according to the scales of pair-wise comparison (as shown in Table 4).
Definition | aij
| Definition | aij
|
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Equally important between i and j | 1 | i is moderately more important than j | 3 |
i is strongly more important than j | 5 | i is very strongly more important than j | 7 |
i is extremely more important than j | 9 |
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Step 2:Consistency check. This process involves four steps.1)Get the largest eigenvalue.
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Step 3:Derive criteria weights as follows:
4.3 Fuzzy comprehensive evaluation model
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Step 1: Simplify the description of health degree as E = {e1, e2 … em}.
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Step 2: Suppose the maximum and minimum value of criterion c is \( {v}_c^{max} \) and \( {v}_c^{min} \) respectively, xc is the benefit-type criterion outcome. Then define the membership function uck (xc) as follows.
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Step 3: Evaluate the micro-level criteria Cij. The weight set is \( {W}_i=\left\{{W}_{i1},{W}_{i2}\dots {W}_{i{ q}_i}\right\} \), and the evaluation vector is B ij = {u ij1, u ij2 … u ijm }.
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Step 4: Evaluate the mid-level criteria Ci. Obtain the membership matrix \( {U}_i={\left({u}_{i jk}\right)}_{q_i\times m} \), and the evaluation vector is B i = {b i1, b i2, … , b im }.
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Step 5: Employ similar procedures to evaluate the macro-level criteria C with W = {W 1, W 2 … W q }, and B = {b 1, b 2 … b m }.
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Step 6: Calculate the final result P, where km is the value of each health degree (equals to 20∗ m).
4.4 Neural network evaluation model
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Step 1:The input vector C = {cij} is normalized into P = {pij}, with fifteen nodes in input layer (micro-layer criteria) and one node in output layer (the goal).
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Step 2:The number of nodes in hidden layer is defined through trails and error, where the initial number is lh, and li, lo is the number of notes in input and output layer, a is a random number from 1 to 10.
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Step 3:Determined transfer functions, and train the model with appropriate learning rate.
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Step 4:Apply test data to evaluate the performance of the designed network. Usually, the mean square error (MSE) was used [57], where yij is the network output for example i at processing element j, and dij is the desired output.
5 Case study
5.1 Empirical data
Criteria | Mean | Max | Min | Std. |
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Order Volatility (%) | 4.62 | 12.45 | 0.76 | 3.39 |
Revenue Growth (%) | 4.65 | 12.06 | −0.14 | 5.93 |
Order Amount | 145,519 | 170,433 | 130,489 | 9120 |
Practitioner | 4874 | 5106 | 4385 | 1,76 |
Operating Vehicles | 3825 | 3930 | 3514 | 1,12 |
Mileage utilization (%) | 65.00 | 71.30 | 59.11 | 2.44 |
Time utilization (%) | 31.31 | 37.72 | 3.30 | 4.61 |
Average operating time (h) | 23.72 | 23.83 | 23.81 | 0.23 |
Average mileage (km) | 401.77 | 428.28 | 393.16 | 14.78 |
Average speed (km/h) | 33.70 | 37.98 | 31.03 | 1.11 |
Total revenue (yuan) | 3,075,752 | 3,650,759 | 2,774,778 | 201,512 |
Average daily income (yuan) | 804.29 | 931.55 | 730.05 | 50.32 |
Income difference (yuan) | 237.44 | 285.81 | 217.07 | 16.26 |
Energy intensity (kg/yuan) | 171.98 | 190.46 | 149.54 | 8.66 |
Carbon intensity (kgCO2/yuan) | 522.82 | 579.02 | 454.59 | 26.34 |
5.2 Synthetic evaluation models
5.2.1 Fuzzy comprehensive evaluation model
Criteria (robustness) | Weight | Criteria (productivity) | Weight | Criteria (sustainability) | Weight |
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Order volatility | 0.16 | Mileage utilization | 0.02 | Total revenue | 0.03 |
Revenue Growth | 0.04 | Time utilization | 0.14 | Average daily income | 0.12 |
Order amount | 0.11 | Average time | 0.06 | Income difference | 0.07 |
Practitioner | 0.04 | Average mileage | 0.08 | Energy intensity | 0.03 |
Operating vehicles | 0.02 | Average speed | 0.06 | Carbon intensity | 0.02 |
5.2.2 Neural network evaluation model
5.3 Sensitivity analysis
6 Conclusions
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There is a trade-off between revenue growth and trips volatility.
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Influence of mileage and time utilization are polarized.
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Total revenue should not be used as single determinant in decision making, since high income difference affects the taxi industry in a harmful way.
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Decision makers should pay more attention to the mount of operating vehicles while keeping a small amount of drivers and administrators.
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The optimum scenario is achieved somewhere around the cross point of the two curves of energy intensity and carbon intensity.