CAR COMMUTERS' MODE CHANGE IN RESPONSE TO TDM MEASURES: EXPERIMENTAL DESIGN APPROACH CONSIDERING TWO-WAY INTERACTIONS

Abstract- Many studies have shown that individuals' responses to urban traffic congestion, as usually assumed by policymakers, are significantly different from their respected actual behavior. This paper adopts a behavioral approach to examine this difference, using the design of experiment pri...

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Veröffentlicht in:Iranian journal of science and technology. Transactions of civil engineering 2013-12, Vol.37 (C), p.479-479
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description Abstract- Many studies have shown that individuals' responses to urban traffic congestion, as usually assumed by policymakers, are significantly different from their respected actual behavior. This paper adopts a behavioral approach to examine this difference, using the design of experiment principles and binary logit models. In this approach, five transportation demand management (TDM) measures including three push and two pull measures were investigated. Then, effects and contributions of the measures in diverting car commuters to seven existing non-car modes were taken into account. This study uses the stated preferences of 288 individuals who regularly use their private cars to access their job locations in the central Tehran area, to calibrate seven non-car mode models. The results show that when considering each mode separately, pull measures are necessary to regulate the market share of each non-car mode. Analysis of the effects of the measures in considering non-car modes shows that although their contributions are about 14% for transit accessed by walking and 7% for taxi, they have never contributed more than 5% to other modes. Table 5 presents a salient representation of the final models of the seven main non-car modes, including Walk & Ride, Drive & Ride, Taxi & Ride, Motorcycle, Drive & Taxi, Taxi and Tel-taxi. Because the focus of this stage is on the general tendency of the measures' effects on consideration of noncar modes, the salient representation of such effects are presented. A positive sign of a variable in these models indicates that the condition represented by that variable increases the relative probability of considering the associated non-car mode. A negative sign also shows that the condition represented by that variable increases the relative probability of considering other non-car modes of the study. The goodness of fit value of models, ρ2, varied in a range of 0.19 for Walk & Ride to 0.91 for Drive & Taxi, which seems suitable in individual-based models. This result is confirmed by assessing each model's lack of fit using Hosmer-Lemeshow (H-L) test, which shows that the lack of fit hypothesis is rejected for each of the models at 5% level of significance. Interactions were all insignificant except when both parking cost and fuel cost measures were implemented simultaneously, which motivated less than 1% of people to choose motorcycles. According to Table 7, a decrease in transit time and improvement in the transit access hav
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This paper adopts a behavioral approach to examine this difference, using the design of experiment principles and binary logit models. In this approach, five transportation demand management (TDM) measures including three push and two pull measures were investigated. Then, effects and contributions of the measures in diverting car commuters to seven existing non-car modes were taken into account. This study uses the stated preferences of 288 individuals who regularly use their private cars to access their job locations in the central Tehran area, to calibrate seven non-car mode models. The results show that when considering each mode separately, pull measures are necessary to regulate the market share of each non-car mode. Analysis of the effects of the measures in considering non-car modes shows that although their contributions are about 14% for transit accessed by walking and 7% for taxi, they have never contributed more than 5% to other modes. Table 5 presents a salient representation of the final models of the seven main non-car modes, including Walk & Ride, Drive & Ride, Taxi & Ride, Motorcycle, Drive & Taxi, Taxi and Tel-taxi. Because the focus of this stage is on the general tendency of the measures' effects on consideration of noncar modes, the salient representation of such effects are presented. A positive sign of a variable in these models indicates that the condition represented by that variable increases the relative probability of considering the associated non-car mode. A negative sign also shows that the condition represented by that variable increases the relative probability of considering other non-car modes of the study. The goodness of fit value of models, ρ2, varied in a range of 0.19 for Walk & Ride to 0.91 for Drive & Taxi, which seems suitable in individual-based models. This result is confirmed by assessing each model's lack of fit using Hosmer-Lemeshow (H-L) test, which shows that the lack of fit hypothesis is rejected for each of the models at 5% level of significance. Interactions were all insignificant except when both parking cost and fuel cost measures were implemented simultaneously, which motivated less than 1% of people to choose motorcycles. According to Table 7, a decrease in transit time and improvement in the transit access have the greatest effects on the mode change of car users. In fact, pull measures that encourage people to consider transit mode usage show higher and more significant effects. As in binary modeling, which assists in distinguishing the effective variables in each mode consideration separately, push measures were not expected to be significant. In fact, these measures are only responsible for transferring individuals away from the Car mode and not for the attraction to specific mode. In contrast, pull measures are the ones that attract drivers to other modes, which appear more effective in this study. The contribution of the measure variables to mode change is an issue assessed in this section. It is worth noting that the high values of goodness of fit indices in some of the models may depend on the imbalance in considerations (i.e., few or many) of the studied modes [24]. In this paper, the method based on the information-theoretic interpretation of ρ2 is adopted to assess the contributions of the measure variables [25]. To find the range of such variables contributions, forward inclusion and backward exclusion methods are adopted. As a priori, one may expect the backward approach to provide the lower bound (since the variables remaining after the exclusion of the measure variables could be somewhat correlated with the excluded variables and hence assume some of the explanatory power of those variables) and the forward stepwise approach to provide the upper bound (since having only the measure variables in the model should allow them to carry some of the explanatory power of the excluded variables with which they are correlated) [6]. However, that was true in only four of the seven cases. Table 8 shows the results of this method. The first and second rows present the goodness of fit of the final (ρ2p) and market-share (ρ2 MS) models. The third row shows the goodness of fit of the model with only measure variables in addition to constants by the forward inclusion approach. The fourth row shows the goodness of fit of the model without measure variables by the backward exclusion approach. The fifth and sixth rows present the goodness of fit improvements resulting from measure variables by forward and backward approaches, respectively.]]></description><identifier>ISSN: 2228-6160</identifier><language>eng</language><publisher>Shiraz: Springer Nature B.V</publisher><subject>Calibration ; Central business districts ; Cities ; Commuting ; Costs ; Design of experiments ; Driving ; Energy consumption ; Highway transportation ; Logit models ; Markets ; Preferences ; Public transportation ; Studies ; Time division multiplexing ; Transportation planning ; Urban transportation</subject><ispartof>Iranian journal of science and technology. Transactions of civil engineering, 2013-12, Vol.37 (C), p.479-479</ispartof><rights>Copyright Iranian Journal of Science and Technology Dec 2013</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,776,780</link.rule.ids></links><search><creatorcontrib>Habibian, M</creatorcontrib><creatorcontrib>Kermanshah, M</creatorcontrib><title>CAR COMMUTERS' MODE CHANGE IN RESPONSE TO TDM MEASURES: EXPERIMENTAL DESIGN APPROACH CONSIDERING TWO-WAY INTERACTIONS</title><title>Iranian journal of science and technology. Transactions of civil engineering</title><description><![CDATA[Abstract- Many studies have shown that individuals' responses to urban traffic congestion, as usually assumed by policymakers, are significantly different from their respected actual behavior. This paper adopts a behavioral approach to examine this difference, using the design of experiment principles and binary logit models. In this approach, five transportation demand management (TDM) measures including three push and two pull measures were investigated. Then, effects and contributions of the measures in diverting car commuters to seven existing non-car modes were taken into account. This study uses the stated preferences of 288 individuals who regularly use their private cars to access their job locations in the central Tehran area, to calibrate seven non-car mode models. The results show that when considering each mode separately, pull measures are necessary to regulate the market share of each non-car mode. Analysis of the effects of the measures in considering non-car modes shows that although their contributions are about 14% for transit accessed by walking and 7% for taxi, they have never contributed more than 5% to other modes. Table 5 presents a salient representation of the final models of the seven main non-car modes, including Walk & Ride, Drive & Ride, Taxi & Ride, Motorcycle, Drive & Taxi, Taxi and Tel-taxi. Because the focus of this stage is on the general tendency of the measures' effects on consideration of noncar modes, the salient representation of such effects are presented. A positive sign of a variable in these models indicates that the condition represented by that variable increases the relative probability of considering the associated non-car mode. A negative sign also shows that the condition represented by that variable increases the relative probability of considering other non-car modes of the study. The goodness of fit value of models, ρ2, varied in a range of 0.19 for Walk & Ride to 0.91 for Drive & Taxi, which seems suitable in individual-based models. This result is confirmed by assessing each model's lack of fit using Hosmer-Lemeshow (H-L) test, which shows that the lack of fit hypothesis is rejected for each of the models at 5% level of significance. Interactions were all insignificant except when both parking cost and fuel cost measures were implemented simultaneously, which motivated less than 1% of people to choose motorcycles. According to Table 7, a decrease in transit time and improvement in the transit access have the greatest effects on the mode change of car users. In fact, pull measures that encourage people to consider transit mode usage show higher and more significant effects. As in binary modeling, which assists in distinguishing the effective variables in each mode consideration separately, push measures were not expected to be significant. In fact, these measures are only responsible for transferring individuals away from the Car mode and not for the attraction to specific mode. In contrast, pull measures are the ones that attract drivers to other modes, which appear more effective in this study. The contribution of the measure variables to mode change is an issue assessed in this section. It is worth noting that the high values of goodness of fit indices in some of the models may depend on the imbalance in considerations (i.e., few or many) of the studied modes [24]. In this paper, the method based on the information-theoretic interpretation of ρ2 is adopted to assess the contributions of the measure variables [25]. To find the range of such variables contributions, forward inclusion and backward exclusion methods are adopted. As a priori, one may expect the backward approach to provide the lower bound (since the variables remaining after the exclusion of the measure variables could be somewhat correlated with the excluded variables and hence assume some of the explanatory power of those variables) and the forward stepwise approach to provide the upper bound (since having only the measure variables in the model should allow them to carry some of the explanatory power of the excluded variables with which they are correlated) [6]. However, that was true in only four of the seven cases. Table 8 shows the results of this method. The first and second rows present the goodness of fit of the final (ρ2p) and market-share (ρ2 MS) models. The third row shows the goodness of fit of the model with only measure variables in addition to constants by the forward inclusion approach. The fourth row shows the goodness of fit of the model without measure variables by the backward exclusion approach. The fifth and sixth rows present the goodness of fit improvements resulting from measure variables by forward and backward approaches, respectively.]]></description><subject>Calibration</subject><subject>Central business districts</subject><subject>Cities</subject><subject>Commuting</subject><subject>Costs</subject><subject>Design of experiments</subject><subject>Driving</subject><subject>Energy consumption</subject><subject>Highway transportation</subject><subject>Logit models</subject><subject>Markets</subject><subject>Preferences</subject><subject>Public transportation</subject><subject>Studies</subject><subject>Time division multiplexing</subject><subject>Transportation planning</subject><subject>Urban transportation</subject><issn>2228-6160</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2013</creationdate><recordtype>article</recordtype><sourceid>8G5</sourceid><sourceid>BENPR</sourceid><sourceid>GUQSH</sourceid><sourceid>M2O</sourceid><recordid>eNpdjs9LwzAYhntQcMz9DwEPeinkV5PqLaSxK7RJaTOmp5G1DTjqNtf1_19AT55e-N6H53vvogXGOI0ZYvAhWk3TAUKIICeQpYtolqIB0lTVxqqmfQaVyRSQa6FzBQoNGtXWRrcKWANsVoFKiXYTjm9AfdSqKSqlrShBptoi10DUdWOEXAehboss9DoHdmvirfgMtvBBSFuE7jG6926chtVfLqPNu7JyHZcmL6Qo4zNG7Bp7x5AbUJr2LKF9l9C0p6yn2DFH96z3SYcITz1DHvM9JdR5hD3b4855nHQuIcvo5dd7vpx-5mG67r6_pm4YR3ccTvO0QwmGr5hzjgL69A89nObLMawLFMScIAoJuQEUp1s7</recordid><startdate>20131201</startdate><enddate>20131201</enddate><creator>Habibian, M</creator><creator>Kermanshah, M</creator><general>Springer Nature B.V</general><scope>3V.</scope><scope>7WY</scope><scope>7XB</scope><scope>883</scope><scope>8AF</scope><scope>8FD</scope><scope>8FE</scope><scope>8FG</scope><scope>8FK</scope><scope>8FL</scope><scope>8G5</scope><scope>ABJCF</scope><scope>ABUWG</scope><scope>AEUYN</scope><scope>AFKRA</scope><scope>ATCPS</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>BEZIV</scope><scope>BGLVJ</scope><scope>BHPHI</scope><scope>CCPQU</scope><scope>CWDGH</scope><scope>DWQXO</scope><scope>FR3</scope><scope>FRNLG</scope><scope>GNUQQ</scope><scope>GUQSH</scope><scope>HCIFZ</scope><scope>K60</scope><scope>K6~</scope><scope>KR7</scope><scope>L.-</scope><scope>L6V</scope><scope>M0F</scope><scope>M2O</scope><scope>M7S</scope><scope>MBDVC</scope><scope>PADUT</scope><scope>PATMY</scope><scope>PQBIZ</scope><scope>PQBZA</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PTHSS</scope><scope>PYCSY</scope><scope>PYYUZ</scope><scope>Q9U</scope><scope>7SC</scope><scope>7TB</scope><scope>H8D</scope><scope>JQ2</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope></search><sort><creationdate>20131201</creationdate><title>CAR COMMUTERS' MODE CHANGE IN RESPONSE TO TDM MEASURES: EXPERIMENTAL DESIGN APPROACH CONSIDERING TWO-WAY INTERACTIONS</title><author>Habibian, M ; 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Transactions of civil engineering</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Habibian, M</au><au>Kermanshah, M</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>CAR COMMUTERS' MODE CHANGE IN RESPONSE TO TDM MEASURES: EXPERIMENTAL DESIGN APPROACH CONSIDERING TWO-WAY INTERACTIONS</atitle><jtitle>Iranian journal of science and technology. Transactions of civil engineering</jtitle><date>2013-12-01</date><risdate>2013</risdate><volume>37</volume><issue>C</issue><spage>479</spage><epage>479</epage><pages>479-479</pages><issn>2228-6160</issn><abstract><![CDATA[Abstract- Many studies have shown that individuals' responses to urban traffic congestion, as usually assumed by policymakers, are significantly different from their respected actual behavior. This paper adopts a behavioral approach to examine this difference, using the design of experiment principles and binary logit models. In this approach, five transportation demand management (TDM) measures including three push and two pull measures were investigated. Then, effects and contributions of the measures in diverting car commuters to seven existing non-car modes were taken into account. This study uses the stated preferences of 288 individuals who regularly use their private cars to access their job locations in the central Tehran area, to calibrate seven non-car mode models. The results show that when considering each mode separately, pull measures are necessary to regulate the market share of each non-car mode. Analysis of the effects of the measures in considering non-car modes shows that although their contributions are about 14% for transit accessed by walking and 7% for taxi, they have never contributed more than 5% to other modes. Table 5 presents a salient representation of the final models of the seven main non-car modes, including Walk & Ride, Drive & Ride, Taxi & Ride, Motorcycle, Drive & Taxi, Taxi and Tel-taxi. Because the focus of this stage is on the general tendency of the measures' effects on consideration of noncar modes, the salient representation of such effects are presented. A positive sign of a variable in these models indicates that the condition represented by that variable increases the relative probability of considering the associated non-car mode. A negative sign also shows that the condition represented by that variable increases the relative probability of considering other non-car modes of the study. The goodness of fit value of models, ρ2, varied in a range of 0.19 for Walk & Ride to 0.91 for Drive & Taxi, which seems suitable in individual-based models. This result is confirmed by assessing each model's lack of fit using Hosmer-Lemeshow (H-L) test, which shows that the lack of fit hypothesis is rejected for each of the models at 5% level of significance. Interactions were all insignificant except when both parking cost and fuel cost measures were implemented simultaneously, which motivated less than 1% of people to choose motorcycles. According to Table 7, a decrease in transit time and improvement in the transit access have the greatest effects on the mode change of car users. In fact, pull measures that encourage people to consider transit mode usage show higher and more significant effects. As in binary modeling, which assists in distinguishing the effective variables in each mode consideration separately, push measures were not expected to be significant. In fact, these measures are only responsible for transferring individuals away from the Car mode and not for the attraction to specific mode. In contrast, pull measures are the ones that attract drivers to other modes, which appear more effective in this study. The contribution of the measure variables to mode change is an issue assessed in this section. It is worth noting that the high values of goodness of fit indices in some of the models may depend on the imbalance in considerations (i.e., few or many) of the studied modes [24]. In this paper, the method based on the information-theoretic interpretation of ρ2 is adopted to assess the contributions of the measure variables [25]. To find the range of such variables contributions, forward inclusion and backward exclusion methods are adopted. As a priori, one may expect the backward approach to provide the lower bound (since the variables remaining after the exclusion of the measure variables could be somewhat correlated with the excluded variables and hence assume some of the explanatory power of those variables) and the forward stepwise approach to provide the upper bound (since having only the measure variables in the model should allow them to carry some of the explanatory power of the excluded variables with which they are correlated) [6]. However, that was true in only four of the seven cases. Table 8 shows the results of this method. The first and second rows present the goodness of fit of the final (ρ2p) and market-share (ρ2 MS) models. The third row shows the goodness of fit of the model with only measure variables in addition to constants by the forward inclusion approach. The fourth row shows the goodness of fit of the model without measure variables by the backward exclusion approach. The fifth and sixth rows present the goodness of fit improvements resulting from measure variables by forward and backward approaches, respectively.]]></abstract><cop>Shiraz</cop><pub>Springer Nature B.V</pub><tpages>1</tpages></addata></record>
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subjects Calibration
Central business districts
Cities
Commuting
Costs
Design of experiments
Driving
Energy consumption
Highway transportation
Logit models
Markets
Preferences
Public transportation
Studies
Time division multiplexing
Transportation planning
Urban transportation
title CAR COMMUTERS' MODE CHANGE IN RESPONSE TO TDM MEASURES: EXPERIMENTAL DESIGN APPROACH CONSIDERING TWO-WAY INTERACTIONS
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