Trust and Distress Prediction in Modal Shift Potential of Long-Distance Road Freight in Containers: Modeling Approach in Transport Services for Sustainability
Abstract
:1. Introduction
2. Literature Background
- The need to halt the dominance of road transport in the transport needs of society and the economy; road transport is characterized by a relatively high environmental impact and affects the barriers to supply of services of modes (congestion, occupancy of area).
- The occurrence of high-substitutability services of inland (road and rail) and water transport.
- High complementarity between modes and means of transport.
- freight—from road transport to water or rail transport;
- people/passengers—from the use of passenger cars to public transport.
3. Data, Methods, and Steps
- Trust:
- one of the dichotomous values for the DT variable for trust status, corresponds with value equal to 1;
- Distress:
- one of the dichotomous values for the DT variable for distress status, corresponds with value equal to 0;
- DT:
- qualitative dependent variable that is vector-encoded (dummy variable); takes the value equal to “Trust” (trust status, not distress) or “Distress” (distress status, not trust); to specify the value, the data from Eurostat database for the modal shift potential of long-distance road freight in containers [tran_im_mosp] were used (percentage of total tkm). In the event that this structure ratio has not increased in relation to the reference period (2011), the variable DT was equal to “Trust” (1); if has increased, then it took the value equal to “Distress” (0);
- TS:
- continuous predictor; modal shift potential of long-distance road freight in containers (in percentage of total tkm); data from Eurostat database [tran_im_mosp];
- RFTG:
- continuous predictor; road freight transport intensity (in tkm per 1000 units of current USD GDP); data from OECD.Stat [..IND-Meas-Roadgood-GDP];
- SRFT:
- continuous predictor; share of road freight transport in total inland freight transport (in percentage); data from OECD.Stat [..IND-Meas-Roadgood-Share];
- CO2EG:
- continuous predictor; CO2 emissions from transport (in tonnes per 1,000,000 units of current USD GDP); data from OECD.Stat [..IND-Ene-GDP];
- SCO2:
- continuous predictor; share of CO2 emissions from road in total CO2 emissions from transport (in percentage), data from OECD.Stat [..IND-Ene-Road];
- ENRTG:
- continuous predictor; energy intensity of road transport expressed by ratio of motor fuel deliveries (in tonnes per 1,000,000 units of current USD GDP); data from OECD.Stat [..IND-Ene-Fuel-GDP].
- low-for 0.00, utility is 0.00,
- indirect: for 0.50, utility equal to 0.50,
- high: for 1.00, utility 1.00
4. Empirical Results
5. Discussion
6. Conclusions
- It is necessary to carry out expert opinions related to the implementation of the paradigm shift and set goals and scope of implementation of the paradigm’s assumptions.
- Each country has a different specificity, but there are also points of contact with other countries. Therefore, all kinds of analyses could be carried out both internationally and in-depth on a national basis.
- It is also important to develop own methodology: unique for a given country and universal for different countries for comparison purposes.
- It is necessary to deepen the discussion on the selection of analysis tools in light of the assessment of the implementation of the paradigm shift.
- Based on the research, it is worth knowing the implications for the chosen scenario and create a decision support system.
- Using utility profiles, it is possible to analyze tolerance ranges and suggest tools that limit or extend tolerance margins.
Author Contributions
Funding
Conflicts of Interest
References
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Country | 2011 | 2012 | 2013 | 2014 | 2015 |
---|---|---|---|---|---|
Bulgaria | Trust | Trust | Trust | Trust | Trust |
Czech Republic | Trust | Distress | Distress | Trust | Trust |
Finland | Trust | Distress | Trust | Distress | Trust |
France | Trust | Trust | Distress | Distress | Trust |
Hungary | Trust | Distress | Distress | Distress | Distress |
Latvia | Trust | Distress | Trust | Trust | Trust |
Lithuania | Trust | Distress | Distress | Distress | Trust |
Luxembourg | Trust | Distress | Trust | Trust | Trust |
Netherlands | Trust | Trust | Trust | Trust | Trust |
Poland | Trust | Trust | Distress | Distress | Trust |
Portugal | Trust | Trust | Trust | Trust | Trust |
Slovak Republic | Trust | Trust | Trust | Trust | Trust |
Slovenia | Trust | Distress | Trust | Trust | Distress |
Spain | Trust | Trust | Distress | Trust | Trust |
Sweden | Trust | Trust | Distress | Trust | Trust |
United Kingdom | Trust | Distress | Trust | Trust | Trust |
Country | 2011 | 2012 | 2013 | 2014 | 2015 |
---|---|---|---|---|---|
Bulgaria | 1.0509 | 0.8743 | 0.9040 | 0.9104 | 1.2677 |
Czech Republic | 1.1539 | 1.4682 | 1.4597 | 1.0809 | 1.1800 |
Finland | 1.2261 | 1.4941 | 1.1449 | 1.4995 | 1.2797 |
France | 1.1918 | 1.1200 | 1.4784 | 1.5034 | 1.1979 |
Hungary | 1.1646 | 1.4722 | 1.4447 | 1.4739 | 1.5637 |
Latvia | 1.4598 | 1.6924 | 1.2742 | 1.2750 | 1.3906 |
Lithuania | 1.3194 | 1.5994 | 1.6095 | 1.6149 | 1.3956 |
Luxembourg | 1.3144 | 1.5965 | 1.1838 | 1.1881 | 1.2510 |
Netherlands | 1.3930 | 1.3373 | 1.3369 | 1.3530 | 1.4444 |
Poland | 1.2312 | 1.1528 | 1.5352 | 1.5337 | 1.2991 |
Portugal | 1.1811 | 1.1163 | 1.0778 | 1.1056 | 1.1961 |
Slovak Republic | 1.3350 | 1.3252 | 1.1364 | 1.1790 | 1.2453 |
Slovenia | 1.4236 | 1.7351 | 1.3847 | 1.3661 | 1.7967 |
Spain | 1.2731 | 1.1786 | 1.5885 | 1.1301 | 1.1964 |
Sweden | 1.2385 | 1.1674 | 1.5128 | 1.1998 | 1.2667 |
United Kingdom | 1.2335 | 1.5241 | 1.1573 | 1.2246 | 1.2884 |
Variable | Mean | Standard Deviation | Variation Coefficient |
---|---|---|---|
TS | 53.9900 | 17.4480 | 32.3171 |
RFTG | 227.7875 | 181.8065 | 79.8141 |
SRFT | 70.2575 | 19.4214 | 27.6431 |
CO2EG | 76.9625 | 31.6230 | 41.0888 |
SCO2 | 95.0375 | 3.3246 | 3.4982 |
ENRTG | 23.5375 | 10.4093 | 44.2242 |
Effect | Steps | F to Put | P to Put | Decision |
---|---|---|---|---|
TS | Step 1 | 7.89943 | 0.006250 | Entered |
RFTG | 0.44796 | 0.505283 | Outside | |
SRFT | 0.32878 | 0.568026 | Outside | |
CO2EG | 0.00414 | 0.948879 | Outside | |
SCO2 | 1.09327 | 0.298977 | Outside | |
ENRTG | 1.13102 | 0.290839 | Outside | |
TS | Step 2 | In model | ||
RFTG | 1.85578 | 0.177084 | Outside | |
SRFT | 0.08135 | 0.776241 | Outside | |
CO2EG | 11.43696 | 0.001134 | Entered | |
SCO2 | 0.80317 | 0.372940 | Outside | |
ENRTG | 0.65892 | 0.419444 | Outside | |
TS | Step 3 | In model | ||
CO2EG | In model | |||
SRFT | 0.00513 | 0.943069 | Outside | |
RFTG | 0.40656 | 0.525637 | Outside | |
SCO2 | 1.67220 | 0.199882 | Outside | |
ENRTG | 4.20252 | 0.043813 | Entered | |
TS | Step 4 | In model | ||
CO2EG | In model | |||
ENRTG | In model | |||
RFTG | 0.28034 | 0.598040 | Outside | |
CO2EG | 0.16052 | 0.689815 | Outside | |
SRFT | 0.26922 | 0.605384 | Outside |
Effect | Trust Parameter | Trust Standard Deviation | Trust t | Trust p-Value | Trust β | Trust Standard Deviation β |
Const | 1.1479 | 0.1463 | 7.8437 | 0.0000 | ||
TS | −0.0172 | 0.0038 | −4.5288 | 0.0000 | −0.6671 | 0.1473 |
CO2EG | 0.0113 | 0.0029 | 3.9158 | 0.0002 | 0.7937 | 0.2027 |
ENRTG | −0.0154 | 0.0075 | −2.0500 | 0.0438 | −0.3576 | 0.1744 |
Effect | Distress Parameter | Distress Standard Deviation | Distress t | Distress p-Value | Distress β | Distress Standard Deviation β |
Const | −0.1479 | 0.1463 | −1.0105 | 0.3155 1 | ||
TS | 0.0172 | 0.0038 | 4.5288 | 0.0000 | 0.6671 | 0.1473 |
CO2EG | −0.0113 | 0.0029 | −3.9158 | 0.0002 | −0.7937 | 0.2027 |
ENRTG | 0.0154 | 0.0075 | 2.0500 | 0.0438 | 0.3576 | 0.1744 |
Variable | Mean | Standard Deviation | Variation Coefficient | Mean | Standard Deviation | Variation Coefficient |
---|---|---|---|---|---|---|
Trust (p = 0.7250) | Distress (p = 0.2750) | |||||
TS | 50.7517 | 17.6560 | 34.7890 | 62.5273 | 13.9207 | 22.2634 |
CO2EG | 77.1035 | 34.0808 | 44.2014 | 76.5909 | 24.6802 | 32.2234 |
ENRTG | 22.7759 | 10.8871 | 47.8010 | 25.5455 | 8.9481 | 35.0281 |
Effect | Test | Value | F | Effect-df | Error-df | p |
---|---|---|---|---|---|---|
Const | Wilks | 0.5526 | 61.5232 | 1 | 76 | 0.0000 |
Pillai | 0.4474 | 61.5232 | 1 | 76 | 0.0000 | |
Hotelling | 0.8095 | 61.5232 | 1 | 76 | 0.0000 | |
Roy | 0.8095 | 61.5232 | 1 | 76 | 0.0000 | |
TS | Wilks | 0.7875 | 20.5101 | 1 | 76 | 0.0000 |
Pillai | 0.2125 | 20.5101 | 1 | 76 | 0.0000 | |
Hotelling | 0.2699 | 20.5101 | 1 | 76 | 0.0000 | |
Roy | 0.2699 | 20.5101 | 1 | 76 | 0.0000 | |
CO2EG | Wilks | 0.8321 | 15.3336 | 1 | 76 | 0.0002 |
Pillai | 0.1679 | 15.3336 | 1 | 76 | 0.0002 | |
Hotelling | 0.2018 | 15.3336 | 1 | 76 | 0.0002 | |
Roy | 0.2018 | 15.3336 | 1 | 76 | 0.0002 | |
ENRTG | Wilks | 0.9476 | 4.2025 | 1 | 76 | 0.0438 |
Pillai | 0.0524 | 4.2025 | 1 | 76 | 0.0438 | |
Hotelling | 0.0553 | 4.2025 | 1 | 76 | 0.0438 | |
Roy | 0.0553 | 4.2025 | 1 | 76 | 0.0438 |
Class | Percent-Correct | Trust | Distress |
---|---|---|---|
Trust | 93.10345 | 54.00000 | 4.00000 |
Distress | 45.45455 | 12.00000 | 10.00000 |
Totality | 80.00000 | 66.00000 | 14.00000 |
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Szaruga, E.; Skąpska, E.; Załoga, E.; Matwiejczuk, W. Trust and Distress Prediction in Modal Shift Potential of Long-Distance Road Freight in Containers: Modeling Approach in Transport Services for Sustainability. Sustainability 2018, 10, 2370. https://doi.org/10.3390/su10072370
Szaruga E, Skąpska E, Załoga E, Matwiejczuk W. Trust and Distress Prediction in Modal Shift Potential of Long-Distance Road Freight in Containers: Modeling Approach in Transport Services for Sustainability. Sustainability. 2018; 10(7):2370. https://doi.org/10.3390/su10072370
Chicago/Turabian StyleSzaruga, Elżbieta, Elżbieta Skąpska, Elżbieta Załoga, and Wiesław Matwiejczuk. 2018. "Trust and Distress Prediction in Modal Shift Potential of Long-Distance Road Freight in Containers: Modeling Approach in Transport Services for Sustainability" Sustainability 10, no. 7: 2370. https://doi.org/10.3390/su10072370