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Workplace relocation from suburb to city center: A case study of Rome, Italy
Case Studies on Transport Policy 7 (2019) 357–362
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Workplace relocation from suburb to city center: A case study of Rome, Italy ⁎
T
S.M. Patella , S. Sportiello, M. Petrelli, S. Carrese Department of Engineering, Roma Tre University, Via Vito Volterra 62, Rome 00146, Italy
A B S T R A C T
As metropolitan areas are generally characterized by decentralized jobs because of a car-based living, in this paper we focus on the feasibility of a “reverse” workplace relocation. The case study of a firm relocation from Rome’s suburbs to the historic city centre is presented. The new location, where the employees will have to move, is in a restricted traffic zone. A behavioural-based method was developed to forecast the future mode split of the employees of the firm being relocated. Next, the model’s estimates were compared with the results from a focus group interview. This approach is expected to provide reliable estimates of the mode choice, as it combines the standard discrete choice modelling for the mid-term forecast with the results from a focus group interview for the short-term forecast. The results from the discrete choice model overestimated the choice of public transport, whereas from the focus group interview it emerged that park & ride is expected to be the most chosen mode in the short-term. Finally, this case study suggests that a workplace relocation from the suburbs to the center could encourage the employees to lessen car-dependent habits.
1. Introduction Since the beginning of the 20th century, cities began to expand to new areas outside the city centre. An overview of the causes of urban spatial expansion can be found in Mieszkowski and Mills (1993). Brueckner (2000) argues that suburbanization results mainly from three powerful forces: growing population, rising incomes, and falling commuting costs. At first, people moved to suburbs but continued to work in the city center, and then firms followed the population. According to Mieszkowski and Mills (1993) the decentralization of residential activity and job suburbanization have a mutual connection, as this process is self-reinforcing: as large employers became suburbanized, their employees followed them. An analysis of the interaction between job and population decentralization can be found in Thurston and Yezer (1994). The suburbanization is closely related to the dominance of transportation by private automotive vehicles. Freund and Martin (1993) argue that: “modernist urban landscapes were built to facilitate automobility and discourage other forms of human movement”. Sheller and Urry (2000) observed that suburbanization resulted in ‘auto sprawl syndrome’ that make people living in suburbs dependent upon the use of cars. Glaeser and Kahn (2004) highlighted that the sprawl is an inexorable product of car-based living. Naess (2006), Kopecky and Suen (2010), Walks (2014), and many other authors confirmed this. A comprehensive analysis of what generated the car dominance mechanism in urban environment can be found in Dupuy (1999), GenreGrandpierre (2007), and Urry (2006). Recently a new phenomenon in world’s developed cities has been recognized and called ‘peak car’. This term was coined by Millard-Ball
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and Schipper (2011) to indicate a decrease in automobile travel demand, and many studies were published on this subject (Metz, 2010; Goodwin, 2011; Newman and Kenworthy, 2011; Schipper, 2011). Metz (2013) observed that the peak car is a manifestation of the transition from the third to the fourth era of travel, in which the car use has ceased to grow. This trend seems to be mainly, but not exclusively, observable among young adults in many Western European countries and in the United States (Bastian et al. 2016; Kuhnimhof et al. 2012). In many developing countries car ownership is expected to continue to grow (Metz, 2015). As for the Italian case, in 1970 car/population ratio was 0.19 and by 1992 it had risen dramatically to 0.51 (Dargay and Gately, 1999), and Italian driving rates peaked in 2000 (Focas and Christidis, 2017). In the city of Rome, the share of car use has slightly decreased from 51.3% in 2009 to 49.3% in 2015 (STATUS, 2016). According to Salvati (2013) Rome is an example of sprawled city; in Rome the level of automobile ownerships is similar to U.S. cities, and motorcycles are largely diffused like many developing countries’ cities (Gori et al., 2012). The transit network of Rome has a radial structure and the public transport services are not yet adapted to efficiently serve many spread-out suburbs. A more detailed description of the transit network of Rome can be found in Cipriani et al. (2012). Given that the job decentralization has been recognized to generate suburbanization and car-dependent life styles, it is reasonable to suppose that an opposite direction of workplaces relocation (i.e. from suburbs to the city center) might reduce car travel. This case study of Rome analyses an opposite direction of workplace relocation: from the suburbs to the city centre. This work presents a relocation case study of a well-known company operating in
Corresponding author. E-mail address: [email protected] (S.M. Patella).
https://doi.org/10.1016/j.cstp.2019.04.009 Received 22 January 2019; Received in revised form 2 April 2019; Accepted 30 April 2019 Available online 02 May 2019 2213-624X/ © 2019 World Conference on Transport Research Society. Published by Elsevier Ltd. All rights reserved.
Case Studies on Transport Policy 7 (2019) 357–362
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distribution of television programs, from the current workplace, located in a suburban area of the city (a car-friendly zone), to the new location in the old town. The new location is inside a small area named ‘Tridente1”, which is in a restricted driving zone (Limited Traffic Zone – LTZ). The map in Fig. 1 shows this area, the location of the enforcement cameras used to detect unauthorized car accesses, and the new location of the workplace. What makes this location relevant to our findings is not only the traffic restriction of this area, but also the structure of the transit network, which is characterized by radial rail axes (underground and urban railway) that converge in the city center (Fig. 2). Vale (2013) highlighted that only few scientific publications dealt with workplace relocation. Others have examined the residential relocation as a result of long commuting distance (Zax and Kain, 1991; Massot et al. 2006). Moreover, Sprumont et al. (2014) observed that the existing literature mainly addresses the impacts of workplace relocation from the city centre to the suburb. To the best of authors’ knowledge, there is a lack of scientific evidence about the effect of a “reverse” workplace relocation on employee’s behaviour, i.e. from the suburb to the city centre, which is the focus of this case study. In this light, the research presented in this paper contributes to grow the body of literature on this issue. This case study focuses on two principal aspects. Firstly, it shows a methodology to forecast the mode choice of the employees in the case of a workplace relocation from suburbs to city center; consequently, this study underlines possible effects of such a relocation on the urban mobility and provides some policy recommendation to promote less car-dependent habits. The reminder of the paper is organized as follows. Section 2 outlines the method of the study, with results presented in Section 3. Section 4 concludes and summarizes the paper.
Fig. 1. Rome central Limited Traffic Zone (LTZ); ‘Tridente’ area.
relocated, we evaluated the future mode choice of the employees (step 4). Finally, model’s estimates were compared with the results of a focus group. This was a face-to-face group interview with the employees waiting for the relocation. Employees were asked about their future mode choice intentions.
2. Methodology The methodology used was designed to forecast the mode choice of the employees of the company to be relocated. By analyzing the mobility-related behavior of employees who already work in that area (Tridente), we developed a behavioral-based approach to estimate the mode shift of the company about to move. Such method is divided into five stages:
3. Results 3.1. Data analysis and discrete choice model (‘Tridente’ dataset) After data cleaning, a consistent database of 788 respondents was prepared for the aggregate analysis of the ‘Tridente’ dataset. Table 1 shows summary statistics, and Table 2 shows the mode choice distribution of the ‘Tridente’ database. The mode choice analysis was crucial to determine the alternatives to include in the discrete choice model (MNL). As shown in Table 2, employees’ most frequent choices are public transport (PT), park & ride intermodal choice (car followed by public transport), Motorbike and Car. This distribution does not reflect the Roman citizens’ mobility-related behaviour, since it refers only to a restricted and enforced traffic zone which allows access only to battery electric vehicles (BEVs) and public transport. In fact, as mentioned in the introduction, the private car is chosen by 50.2% of the population. The following alternatives were included in the model:
1. Designing and conducting a Revealed Preference (RP) survey addressed to the employees who will move to the new location. This step is crucial to acquire information about their mobility habits, residential location, needs, attitudes, and propension to change. 2. Designing and conducting a RP survey addressed to those employees who already work in various firms in the area where the relocation will take place. This survey is very similar to the previous one, and it generated the ‘Tridente’ dataset. 3. Mode choice modelling from the ‘Tridente’ dataset. A Multinomial Logit (MNL) is used for parameters’ estimation. 4. Estimated coefficients are used to forecast the future mode choices for the employees of the firm that will relocate. 5. Focus group interviews. Comparison of the behavioral model with the focus group findings.
• Public Transport • Intermodality: Car and Public Transport • Car • Motorbike
The methodology scheme is reported in Fig. 3. The surveys were done during 2018, and all the interviewees were contacted via e-mail. We used the time duration for completing the questionnaire as a proxy for reliability (step 1 and 2). Next, a Multinomial Logit model (MNL) is estimated to calculate the probability of the mode split (step 3). By applying the estimated coefficient to the database of the company to be
Walk and cycling were not included, since the number of cases is too small. As before mentioned, the discrete choice model used for the regression was a MNL. The overlap among the alternatives public transport and park & ride suggests that a MNL is not appropriate, as the error terms of the utilities are not independent between these alternatives. Nevertheless, after several tests with more appropriate models such as Cross Nested Logit and Probit, better statistical results have been
1 Three straight streets of Rome, departing from Piazza del Popolo and diverging southward, taking the shape of a trident, form the Tridente (Italian for Trident).
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Fig. 2. Employees’ residential location, old/new workplace location, rail axes.
provided by the MNL. Furthermore, even though the two options “Motorbike” and “Car” could be merged into a single alternative called “Private Transport”, they were kept separately. The reason is that motorbikes are allowed to access in the LTZ and to park near the workplace, unlike cars. As for the regression, the sample used for parameters estimation consisted in 296 individuals from the ‘Tridente’ dataset. This subset was obtained after a data cleaning procedure. In fact, even if 788 respondents declared basic information such as gender, age, mode choice, etc., not all of them provided the residential location (it was not mandatory for privacy reasons), which is crucial for the mode choice modelling. In fact, travel times, distances, public transport coverage, and many other attributes depend on the residential location. The mode
Fig. 3. Methodological scheme. Table 1 Summary statistics. Business area
Number of respondents
Government Institutions Banking Commerce
560 141 87
Total sample
788
Gender
Avg. age
Licensed drivers
Male
Female
Male
Female
Male
Female
39% 36% 36%
61% 64% 64%
53 43 33
52 40 34
38% 35% 29%
55% 61% 48%
choice share of this subset was: public transport 50%, Intermodality 27%, Motorbike 18%, Car 5%. To determine useful attributes among the large amount of information, a correlation analysis was performed. The correlation analysis showed that the attributes “external/internal residential location inside the GRA2”, “trip distance”, “travel times”, and “costs” are strongly dependent, and therefore, only travel times were included in the utility functions. The model estimation found several respondent attributes irrelevant (i.e., statistically insignificant at the 10-percent level) to their mode
Table 2 Mode choice distribution. Mode Choice
Respondents
%
PT Car + PT Variable Motorbike Car Walk Motorbike + PT Bicycle
337 170 111 100 39 11 11 9
43% 22% 14% 13% 5% 1% 1% 1%
2 GRA: Grande Raccordo Anulare, i.e. a ring-shaped, 68.2 km long freeway that encircles Rome (see Fig. 2).
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choice. These included safety perception, daily starting and finishing work time, age, and trip start time. The final utility functions were defined as:
Table 3 Estimation result.
Ucar = βt ∗ Tcar + βstop ∗ stop + ASCcar Umotorbike = βt ∗ Tmotorbike + ASCmotorbike UPT = βt ∗ TPT + βTransfer ∗ NTransfer + βgender ∗ Gender + ASCPT
UIntermodality = βt ∗ Tinter + βstop ∗ stop where U is the Utility, β is the coefficient, T is the travel time, transfer is the number of transfers, ASC is the Alternative Specific Constant, and stop is a binary variable that was assigned a value of 1 if the respondent needs to make a stop during the trip. The estimated coefficients3 values are shown in Table 3. The values are statistically reliable; the ρ2 is equal to 0.5 and the 76% of the sample was correctly predicted. The parameters’ coefficients exhibit reasonable magnitudes and signs with respect to the utility equations. A negative sign for the time coefficient (βt ) is reasonable, since the travel time, which represents a cost, is a disutility for employees. Table 4 compares the average travel time by car and by the chosen mode and their relative difference for all the alternatives included in the model for each group of respondents (grouped by the mode choice). The need to make a stop during the home-to-work trip to perform secondary activities (such as driving children to school, going to the gym, etc.) was a binary variable that was assigned a value of 1 if the employee makes a stop. The utility equations were established such that a positive sign of this coefficient (βstop ) increases the probability of choosing “Car” and “Intermodality”. This result clearly suggests that the car use is better suited to those who need to make stop during the trip. Previous research (Ho and Mulley, 2013) indicated that as the spatial dispersion of the activities increase, the trip chain is more likely to be car oriented. Hensher and Reyes (2000) found that as the trip chains become more complex, the utility of public transport decreases. The number of transfers related to public transport was an integer variable. The utility functions were established such that a negative sing of the related coefficient (βTransfer ) decreases the probability of riding public transport. Indeed, a disutility is perceived by users when making a transfer, as was also confirmed by recent researches (GarciaMartinez et al., 2018; Cheng and Tseng, 2016; Schakenbos et al., 2016). The gender was a binary variable that was assigned a value of 1 if the respondent is a woman. The estimation found a negative-sign coefficient for the gender. As it appears in the expression of the utility function of public transport, it could mean that women feel more uncomfortable in riding public transport. This result might be justified by a perceived insecurity associated with use of and access to public transport (Börjesson, 2012). ASC values are positive for the motorbike and the public transport, but negative for the Car. This reflects the advantage of arriving close to the workplace using public transport and motorbike, as well as drivers’ perceived disutility of looking for a park in a fare required space outside the LTZ and then walking to the workplace.
Name
Value
Std. err
t-test
P-value
ASCcar ASCmotorbike ASCPT βgender
−0.710 3.90 2.40 −0.607
0.334 0.606 0.397 0.346
−2.13 6.43 6.03 −1.76
0.03 0.00 0.00 0.08
βTransfer
−0.820
0.191
−4.29
0.00
βstop
0.932
0.337
2.76
0.01
βt
−0.0691
0.0127
−5.42
0.00
Table 4 Travel times comparison. Mode
N° of users
T¯choice (min)
T¯car (min)
T¯choice − T¯car T¯car
Car Motorbike Park & Ride Public Transport
16 53 79 148
59 22 49 38
59 54 73 51
– −59% −33% −25%
T¯choice : Avg. travel time by the chosen mode. T¯car : Avg. travel time by car.
used. With respect to the car pooling service,4 it is not suitable for a restricted traffic zone, since only BEVs are allowed to get inside it, and the diffusion of Electric Vehicles in Rome is very limited (Asdrubali et al., 2018; Carrese et al., 2017a). However, since only 36% of employees gave a negative opinion on the car pooling service, policies to develop the car pooling for the first part of the trip, i.e. Carpool & Ride (Nurul Habib et al., 2011), should be promoted. Lastly, the use of the “Bicycle” varies according to the employees’ health, the distance to be travelled, and above all the weather conditions. The ‘Tridente’ survey has shown that 51% of employees would not use the bicycle in any case, whereas the remaining part might use it only under certain conditions. Specifically, they would like the Corporate to provide them with electrically assisted bicycles, protected parking spaces, and a locker room. However, these incentives are likely to reveal unuseful, as the quality of cycle infrastructure in Rome is very poor and unsafe, and the bicycle infrastructure (either lanes or marked sections) lacks of continuity. Rome is a bike-unfriendly city, and there are still many infrastructural interventions and policy initiatives to do to make Rome bike-friendly. 3.2. Mode choice prediction (relocated firm dataset) Once the coefficients were estimated, they were applied to the employees of the firm being relocated, in order to forecast the mode choice. To perform this procedure, a new database was built starting from the data obtained by the questionnaire defined in step 1 of the methodology. Some results of this survey are summarized in Table 5. Next, we determined the utility values for each alternative and for each employee using the estimated coefficients obtained by the regression. From the result of the parameters’ estimation (see Table 3), we obtain:
3.1.1. Preference for alternative modes Responses from the Tridente’ database (employees currently working in the old town) were also analysed to evaluate the real potential of alternative modes (sharing mobility and cycling) in such area. As for car sharing service, only 24% of employees have subscribed to the service and, among them, only 18% have used it at least once. In most of cases employees do not consider convenient to car-share for their home-to-work travel, and the car sharing service is occasionally
Vmotorbike = −0.0691 ∗ Tmotorbike + 3.90 VPT = −0.0691 ∗ TPT − 0.82 ∗ Ntransfer − 0.607 ∗ Gender + 2.40 VIntermodality = −0.0691 ∗ Tinter + 0.932 ∗ Stop Vcar = −0.0691 ∗ Tcar + 0.932 ∗ Stop − 0.71
3 Coefficients were estimated using the Biogeme Optimization toolbox (Bierlaire, 2013).
4 The diffusion of the car pooling in Rome was investigated by Carrese et al. (2017b).
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Table 5 Employees of the firm to be relocated – survey summary statistics. Business area
Number of respondents
Television distribution
143
Total sample
143
Gender
Avg. age
Licensed drivers
Male
Female
Male
Female
Male
Female
44%
56%
40
38
95%
93%
expected to provide reliable estimates of the mode choice, as it combines standard discrete choice modelling for a mid-term forecast and the results from a focus group interview for the short-term forecast. The Multinomial Logit model used for the regression provided reasonable coefficients’ sign and magnitude. The comparison between the results of the behavioural model and the focus group interview showed that the relocated employees, especially during in short-term, are inclined to keep choosing the mode they were used to. Since the new location is inside a restricted driving zone (no cars allowed), the employees who will undergo greater changes in their mobility-related habits are those who used to drive to the workplace. This is due both to a generalized disposition to resist changes, i.e. shifting from the car to the public transport, and to the need to perform secondary activities during their home-to-work trip. In this light, the case study presented in this paper contributes to better understand how a “reverse” workplace relocation might affect employees’ mobility-related behaviour. Next, this case study suggests that relocating from suburb to city center, in those cities characterized by a radial transit network, creates extra demand for public transport. However, those employees who were used to drive will suffer more from the relocation, and, in the short-term, will switch to park & ride. From a policy perspective, promoting the workplace relocation from suburb to the city center might generate additional car trips from suburbs to peripheral public transport hubs, and this implies additional park space in such intermodal nodes. For this reasons, such policies should be accompanied by ad hoc interventions to promote the use of car sharing and car pooling for the first part of the journey. The results presented in this study suggests that a workplace relocation from the suburbs to the center could encourage the employees to lessen car-dependent habits. Future research could comparatively investigate for what type of firm such relocation is more suitable.
Table 6 Comparison between focus group revealed intention and model results. Mode Choice
Focus Group
Behavioural Model
Intermodality Public Transport Motorbike Walk/Bike Car
38% 52% 6% 2% 0%
17% 68% 14% – 1%
Finally, we determined the probability of choosing each alternative j for every user i (employee) through the very well-known closed form: pi (j ) = exp V ij / ∑h exp Vhi . For more details, see Train (2009). The alternative with the higher probability represents the employee’s future mode choice. The application of such model for the relocation scenario shows that 68% of employees use public transport, 17% Intermodality, 14% motorbikes and only 1% private cars. 3.3. Focus group findings The mode choice scenario obtained with the behavioural model will probably be reached after a period of adjustment. The results of the discrete choice model overestimate the choice of public transport, whereas from the focus group emerged that park & ride is expected to be the most chosen mode. This is mainly due both to a general resistance to change habits (Oreg, 2003), i.e. shifting from the car to the public transport, and to the need to perform activities during their trip chain. Walker et al. (2014) observed that after workplace relocation travel habits do not disappear at once, but rather decay over a period of weeks. The focus group interviews found that those who used to choose the public transport to reach their previous workplace would keep choosing it, since the new workplace is better served by the transit network (see Fig. 2). As to those who own a motorbike, they will keep using it to their final destination, since the LTZ is accessible by motorbikes. The small number of employees living in the close proximity of the new workplace location will reach it by foot or by bicycle. The majority of the employees, namely those who were used to drive to the old location, will suffer the most from the relocation. As it is no longer possible to reach the new workplace by car, they will choose intermodal transport, which involves car and public transport (park & ride). The focus group results suggested that the intermodal transport choice will be more widely used on the short-term, whereas the mode split scenario provided by the discrete choice model should be achieved after a period of adjustment. As already mentioned, walking and cycling mode were not included in the MNL. All the 143 employees of the firm to be relocated were interviewed. Table 6 shows the comparison between focus group revealed intention and model results.
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4. Conclusion This case study focuses on a workplace relocation from suburb to city center. Firstly, this work provides a methodology to forecast the mode choice of the relocated employees. The proposed approach is 361
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Workplace relocation from suburb to city center: A case study of Rome, Italy ⁎
T
S.M. Patella , S. Sportiello, M. Petrelli, S. Carrese Department of Engineering, Roma Tre University, Via Vito Volterra 62, Rome 00146, Italy
A B S T R A C T
As metropolitan areas are generally characterized by decentralized jobs because of a car-based living, in this paper we focus on the feasibility of a “reverse” workplace relocation. The case study of a firm relocation from Rome’s suburbs to the historic city centre is presented. The new location, where the employees will have to move, is in a restricted traffic zone. A behavioural-based method was developed to forecast the future mode split of the employees of the firm being relocated. Next, the model’s estimates were compared with the results from a focus group interview. This approach is expected to provide reliable estimates of the mode choice, as it combines the standard discrete choice modelling for the mid-term forecast with the results from a focus group interview for the short-term forecast. The results from the discrete choice model overestimated the choice of public transport, whereas from the focus group interview it emerged that park & ride is expected to be the most chosen mode in the short-term. Finally, this case study suggests that a workplace relocation from the suburbs to the center could encourage the employees to lessen car-dependent habits.
1. Introduction Since the beginning of the 20th century, cities began to expand to new areas outside the city centre. An overview of the causes of urban spatial expansion can be found in Mieszkowski and Mills (1993). Brueckner (2000) argues that suburbanization results mainly from three powerful forces: growing population, rising incomes, and falling commuting costs. At first, people moved to suburbs but continued to work in the city center, and then firms followed the population. According to Mieszkowski and Mills (1993) the decentralization of residential activity and job suburbanization have a mutual connection, as this process is self-reinforcing: as large employers became suburbanized, their employees followed them. An analysis of the interaction between job and population decentralization can be found in Thurston and Yezer (1994). The suburbanization is closely related to the dominance of transportation by private automotive vehicles. Freund and Martin (1993) argue that: “modernist urban landscapes were built to facilitate automobility and discourage other forms of human movement”. Sheller and Urry (2000) observed that suburbanization resulted in ‘auto sprawl syndrome’ that make people living in suburbs dependent upon the use of cars. Glaeser and Kahn (2004) highlighted that the sprawl is an inexorable product of car-based living. Naess (2006), Kopecky and Suen (2010), Walks (2014), and many other authors confirmed this. A comprehensive analysis of what generated the car dominance mechanism in urban environment can be found in Dupuy (1999), GenreGrandpierre (2007), and Urry (2006). Recently a new phenomenon in world’s developed cities has been recognized and called ‘peak car’. This term was coined by Millard-Ball
⁎
and Schipper (2011) to indicate a decrease in automobile travel demand, and many studies were published on this subject (Metz, 2010; Goodwin, 2011; Newman and Kenworthy, 2011; Schipper, 2011). Metz (2013) observed that the peak car is a manifestation of the transition from the third to the fourth era of travel, in which the car use has ceased to grow. This trend seems to be mainly, but not exclusively, observable among young adults in many Western European countries and in the United States (Bastian et al. 2016; Kuhnimhof et al. 2012). In many developing countries car ownership is expected to continue to grow (Metz, 2015). As for the Italian case, in 1970 car/population ratio was 0.19 and by 1992 it had risen dramatically to 0.51 (Dargay and Gately, 1999), and Italian driving rates peaked in 2000 (Focas and Christidis, 2017). In the city of Rome, the share of car use has slightly decreased from 51.3% in 2009 to 49.3% in 2015 (STATUS, 2016). According to Salvati (2013) Rome is an example of sprawled city; in Rome the level of automobile ownerships is similar to U.S. cities, and motorcycles are largely diffused like many developing countries’ cities (Gori et al., 2012). The transit network of Rome has a radial structure and the public transport services are not yet adapted to efficiently serve many spread-out suburbs. A more detailed description of the transit network of Rome can be found in Cipriani et al. (2012). Given that the job decentralization has been recognized to generate suburbanization and car-dependent life styles, it is reasonable to suppose that an opposite direction of workplaces relocation (i.e. from suburbs to the city center) might reduce car travel. This case study of Rome analyses an opposite direction of workplace relocation: from the suburbs to the city centre. This work presents a relocation case study of a well-known company operating in
Corresponding author. E-mail address: [email protected] (S.M. Patella).
https://doi.org/10.1016/j.cstp.2019.04.009 Received 22 January 2019; Received in revised form 2 April 2019; Accepted 30 April 2019 Available online 02 May 2019 2213-624X/ © 2019 World Conference on Transport Research Society. Published by Elsevier Ltd. All rights reserved.
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distribution of television programs, from the current workplace, located in a suburban area of the city (a car-friendly zone), to the new location in the old town. The new location is inside a small area named ‘Tridente1”, which is in a restricted driving zone (Limited Traffic Zone – LTZ). The map in Fig. 1 shows this area, the location of the enforcement cameras used to detect unauthorized car accesses, and the new location of the workplace. What makes this location relevant to our findings is not only the traffic restriction of this area, but also the structure of the transit network, which is characterized by radial rail axes (underground and urban railway) that converge in the city center (Fig. 2). Vale (2013) highlighted that only few scientific publications dealt with workplace relocation. Others have examined the residential relocation as a result of long commuting distance (Zax and Kain, 1991; Massot et al. 2006). Moreover, Sprumont et al. (2014) observed that the existing literature mainly addresses the impacts of workplace relocation from the city centre to the suburb. To the best of authors’ knowledge, there is a lack of scientific evidence about the effect of a “reverse” workplace relocation on employee’s behaviour, i.e. from the suburb to the city centre, which is the focus of this case study. In this light, the research presented in this paper contributes to grow the body of literature on this issue. This case study focuses on two principal aspects. Firstly, it shows a methodology to forecast the mode choice of the employees in the case of a workplace relocation from suburbs to city center; consequently, this study underlines possible effects of such a relocation on the urban mobility and provides some policy recommendation to promote less car-dependent habits. The reminder of the paper is organized as follows. Section 2 outlines the method of the study, with results presented in Section 3. Section 4 concludes and summarizes the paper.
Fig. 1. Rome central Limited Traffic Zone (LTZ); ‘Tridente’ area.
relocated, we evaluated the future mode choice of the employees (step 4). Finally, model’s estimates were compared with the results of a focus group. This was a face-to-face group interview with the employees waiting for the relocation. Employees were asked about their future mode choice intentions.
2. Methodology The methodology used was designed to forecast the mode choice of the employees of the company to be relocated. By analyzing the mobility-related behavior of employees who already work in that area (Tridente), we developed a behavioral-based approach to estimate the mode shift of the company about to move. Such method is divided into five stages:
3. Results 3.1. Data analysis and discrete choice model (‘Tridente’ dataset) After data cleaning, a consistent database of 788 respondents was prepared for the aggregate analysis of the ‘Tridente’ dataset. Table 1 shows summary statistics, and Table 2 shows the mode choice distribution of the ‘Tridente’ database. The mode choice analysis was crucial to determine the alternatives to include in the discrete choice model (MNL). As shown in Table 2, employees’ most frequent choices are public transport (PT), park & ride intermodal choice (car followed by public transport), Motorbike and Car. This distribution does not reflect the Roman citizens’ mobility-related behaviour, since it refers only to a restricted and enforced traffic zone which allows access only to battery electric vehicles (BEVs) and public transport. In fact, as mentioned in the introduction, the private car is chosen by 50.2% of the population. The following alternatives were included in the model:
1. Designing and conducting a Revealed Preference (RP) survey addressed to the employees who will move to the new location. This step is crucial to acquire information about their mobility habits, residential location, needs, attitudes, and propension to change. 2. Designing and conducting a RP survey addressed to those employees who already work in various firms in the area where the relocation will take place. This survey is very similar to the previous one, and it generated the ‘Tridente’ dataset. 3. Mode choice modelling from the ‘Tridente’ dataset. A Multinomial Logit (MNL) is used for parameters’ estimation. 4. Estimated coefficients are used to forecast the future mode choices for the employees of the firm that will relocate. 5. Focus group interviews. Comparison of the behavioral model with the focus group findings.
• Public Transport • Intermodality: Car and Public Transport • Car • Motorbike
The methodology scheme is reported in Fig. 3. The surveys were done during 2018, and all the interviewees were contacted via e-mail. We used the time duration for completing the questionnaire as a proxy for reliability (step 1 and 2). Next, a Multinomial Logit model (MNL) is estimated to calculate the probability of the mode split (step 3). By applying the estimated coefficient to the database of the company to be
Walk and cycling were not included, since the number of cases is too small. As before mentioned, the discrete choice model used for the regression was a MNL. The overlap among the alternatives public transport and park & ride suggests that a MNL is not appropriate, as the error terms of the utilities are not independent between these alternatives. Nevertheless, after several tests with more appropriate models such as Cross Nested Logit and Probit, better statistical results have been
1 Three straight streets of Rome, departing from Piazza del Popolo and diverging southward, taking the shape of a trident, form the Tridente (Italian for Trident).
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Fig. 2. Employees’ residential location, old/new workplace location, rail axes.
provided by the MNL. Furthermore, even though the two options “Motorbike” and “Car” could be merged into a single alternative called “Private Transport”, they were kept separately. The reason is that motorbikes are allowed to access in the LTZ and to park near the workplace, unlike cars. As for the regression, the sample used for parameters estimation consisted in 296 individuals from the ‘Tridente’ dataset. This subset was obtained after a data cleaning procedure. In fact, even if 788 respondents declared basic information such as gender, age, mode choice, etc., not all of them provided the residential location (it was not mandatory for privacy reasons), which is crucial for the mode choice modelling. In fact, travel times, distances, public transport coverage, and many other attributes depend on the residential location. The mode
Fig. 3. Methodological scheme. Table 1 Summary statistics. Business area
Number of respondents
Government Institutions Banking Commerce
560 141 87
Total sample
788
Gender
Avg. age
Licensed drivers
Male
Female
Male
Female
Male
Female
39% 36% 36%
61% 64% 64%
53 43 33
52 40 34
38% 35% 29%
55% 61% 48%
choice share of this subset was: public transport 50%, Intermodality 27%, Motorbike 18%, Car 5%. To determine useful attributes among the large amount of information, a correlation analysis was performed. The correlation analysis showed that the attributes “external/internal residential location inside the GRA2”, “trip distance”, “travel times”, and “costs” are strongly dependent, and therefore, only travel times were included in the utility functions. The model estimation found several respondent attributes irrelevant (i.e., statistically insignificant at the 10-percent level) to their mode
Table 2 Mode choice distribution. Mode Choice
Respondents
%
PT Car + PT Variable Motorbike Car Walk Motorbike + PT Bicycle
337 170 111 100 39 11 11 9
43% 22% 14% 13% 5% 1% 1% 1%
2 GRA: Grande Raccordo Anulare, i.e. a ring-shaped, 68.2 km long freeway that encircles Rome (see Fig. 2).
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choice. These included safety perception, daily starting and finishing work time, age, and trip start time. The final utility functions were defined as:
Table 3 Estimation result.
Ucar = βt ∗ Tcar + βstop ∗ stop + ASCcar Umotorbike = βt ∗ Tmotorbike + ASCmotorbike UPT = βt ∗ TPT + βTransfer ∗ NTransfer + βgender ∗ Gender + ASCPT
UIntermodality = βt ∗ Tinter + βstop ∗ stop where U is the Utility, β is the coefficient, T is the travel time, transfer is the number of transfers, ASC is the Alternative Specific Constant, and stop is a binary variable that was assigned a value of 1 if the respondent needs to make a stop during the trip. The estimated coefficients3 values are shown in Table 3. The values are statistically reliable; the ρ2 is equal to 0.5 and the 76% of the sample was correctly predicted. The parameters’ coefficients exhibit reasonable magnitudes and signs with respect to the utility equations. A negative sign for the time coefficient (βt ) is reasonable, since the travel time, which represents a cost, is a disutility for employees. Table 4 compares the average travel time by car and by the chosen mode and their relative difference for all the alternatives included in the model for each group of respondents (grouped by the mode choice). The need to make a stop during the home-to-work trip to perform secondary activities (such as driving children to school, going to the gym, etc.) was a binary variable that was assigned a value of 1 if the employee makes a stop. The utility equations were established such that a positive sign of this coefficient (βstop ) increases the probability of choosing “Car” and “Intermodality”. This result clearly suggests that the car use is better suited to those who need to make stop during the trip. Previous research (Ho and Mulley, 2013) indicated that as the spatial dispersion of the activities increase, the trip chain is more likely to be car oriented. Hensher and Reyes (2000) found that as the trip chains become more complex, the utility of public transport decreases. The number of transfers related to public transport was an integer variable. The utility functions were established such that a negative sing of the related coefficient (βTransfer ) decreases the probability of riding public transport. Indeed, a disutility is perceived by users when making a transfer, as was also confirmed by recent researches (GarciaMartinez et al., 2018; Cheng and Tseng, 2016; Schakenbos et al., 2016). The gender was a binary variable that was assigned a value of 1 if the respondent is a woman. The estimation found a negative-sign coefficient for the gender. As it appears in the expression of the utility function of public transport, it could mean that women feel more uncomfortable in riding public transport. This result might be justified by a perceived insecurity associated with use of and access to public transport (Börjesson, 2012). ASC values are positive for the motorbike and the public transport, but negative for the Car. This reflects the advantage of arriving close to the workplace using public transport and motorbike, as well as drivers’ perceived disutility of looking for a park in a fare required space outside the LTZ and then walking to the workplace.
Name
Value
Std. err
t-test
P-value
ASCcar ASCmotorbike ASCPT βgender
−0.710 3.90 2.40 −0.607
0.334 0.606 0.397 0.346
−2.13 6.43 6.03 −1.76
0.03 0.00 0.00 0.08
βTransfer
−0.820
0.191
−4.29
0.00
βstop
0.932
0.337
2.76
0.01
βt
−0.0691
0.0127
−5.42
0.00
Table 4 Travel times comparison. Mode
N° of users
T¯choice (min)
T¯car (min)
T¯choice − T¯car T¯car
Car Motorbike Park & Ride Public Transport
16 53 79 148
59 22 49 38
59 54 73 51
– −59% −33% −25%
T¯choice : Avg. travel time by the chosen mode. T¯car : Avg. travel time by car.
used. With respect to the car pooling service,4 it is not suitable for a restricted traffic zone, since only BEVs are allowed to get inside it, and the diffusion of Electric Vehicles in Rome is very limited (Asdrubali et al., 2018; Carrese et al., 2017a). However, since only 36% of employees gave a negative opinion on the car pooling service, policies to develop the car pooling for the first part of the trip, i.e. Carpool & Ride (Nurul Habib et al., 2011), should be promoted. Lastly, the use of the “Bicycle” varies according to the employees’ health, the distance to be travelled, and above all the weather conditions. The ‘Tridente’ survey has shown that 51% of employees would not use the bicycle in any case, whereas the remaining part might use it only under certain conditions. Specifically, they would like the Corporate to provide them with electrically assisted bicycles, protected parking spaces, and a locker room. However, these incentives are likely to reveal unuseful, as the quality of cycle infrastructure in Rome is very poor and unsafe, and the bicycle infrastructure (either lanes or marked sections) lacks of continuity. Rome is a bike-unfriendly city, and there are still many infrastructural interventions and policy initiatives to do to make Rome bike-friendly. 3.2. Mode choice prediction (relocated firm dataset) Once the coefficients were estimated, they were applied to the employees of the firm being relocated, in order to forecast the mode choice. To perform this procedure, a new database was built starting from the data obtained by the questionnaire defined in step 1 of the methodology. Some results of this survey are summarized in Table 5. Next, we determined the utility values for each alternative and for each employee using the estimated coefficients obtained by the regression. From the result of the parameters’ estimation (see Table 3), we obtain:
3.1.1. Preference for alternative modes Responses from the Tridente’ database (employees currently working in the old town) were also analysed to evaluate the real potential of alternative modes (sharing mobility and cycling) in such area. As for car sharing service, only 24% of employees have subscribed to the service and, among them, only 18% have used it at least once. In most of cases employees do not consider convenient to car-share for their home-to-work travel, and the car sharing service is occasionally
Vmotorbike = −0.0691 ∗ Tmotorbike + 3.90 VPT = −0.0691 ∗ TPT − 0.82 ∗ Ntransfer − 0.607 ∗ Gender + 2.40 VIntermodality = −0.0691 ∗ Tinter + 0.932 ∗ Stop Vcar = −0.0691 ∗ Tcar + 0.932 ∗ Stop − 0.71
3 Coefficients were estimated using the Biogeme Optimization toolbox (Bierlaire, 2013).
4 The diffusion of the car pooling in Rome was investigated by Carrese et al. (2017b).
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Table 5 Employees of the firm to be relocated – survey summary statistics. Business area
Number of respondents
Television distribution
143
Total sample
143
Gender
Avg. age
Licensed drivers
Male
Female
Male
Female
Male
Female
44%
56%
40
38
95%
93%
expected to provide reliable estimates of the mode choice, as it combines standard discrete choice modelling for a mid-term forecast and the results from a focus group interview for the short-term forecast. The Multinomial Logit model used for the regression provided reasonable coefficients’ sign and magnitude. The comparison between the results of the behavioural model and the focus group interview showed that the relocated employees, especially during in short-term, are inclined to keep choosing the mode they were used to. Since the new location is inside a restricted driving zone (no cars allowed), the employees who will undergo greater changes in their mobility-related habits are those who used to drive to the workplace. This is due both to a generalized disposition to resist changes, i.e. shifting from the car to the public transport, and to the need to perform secondary activities during their home-to-work trip. In this light, the case study presented in this paper contributes to better understand how a “reverse” workplace relocation might affect employees’ mobility-related behaviour. Next, this case study suggests that relocating from suburb to city center, in those cities characterized by a radial transit network, creates extra demand for public transport. However, those employees who were used to drive will suffer more from the relocation, and, in the short-term, will switch to park & ride. From a policy perspective, promoting the workplace relocation from suburb to the city center might generate additional car trips from suburbs to peripheral public transport hubs, and this implies additional park space in such intermodal nodes. For this reasons, such policies should be accompanied by ad hoc interventions to promote the use of car sharing and car pooling for the first part of the journey. The results presented in this study suggests that a workplace relocation from the suburbs to the center could encourage the employees to lessen car-dependent habits. Future research could comparatively investigate for what type of firm such relocation is more suitable.
Table 6 Comparison between focus group revealed intention and model results. Mode Choice
Focus Group
Behavioural Model
Intermodality Public Transport Motorbike Walk/Bike Car
38% 52% 6% 2% 0%
17% 68% 14% – 1%
Finally, we determined the probability of choosing each alternative j for every user i (employee) through the very well-known closed form: pi (j ) = exp V ij / ∑h exp Vhi . For more details, see Train (2009). The alternative with the higher probability represents the employee’s future mode choice. The application of such model for the relocation scenario shows that 68% of employees use public transport, 17% Intermodality, 14% motorbikes and only 1% private cars. 3.3. Focus group findings The mode choice scenario obtained with the behavioural model will probably be reached after a period of adjustment. The results of the discrete choice model overestimate the choice of public transport, whereas from the focus group emerged that park & ride is expected to be the most chosen mode. This is mainly due both to a general resistance to change habits (Oreg, 2003), i.e. shifting from the car to the public transport, and to the need to perform activities during their trip chain. Walker et al. (2014) observed that after workplace relocation travel habits do not disappear at once, but rather decay over a period of weeks. The focus group interviews found that those who used to choose the public transport to reach their previous workplace would keep choosing it, since the new workplace is better served by the transit network (see Fig. 2). As to those who own a motorbike, they will keep using it to their final destination, since the LTZ is accessible by motorbikes. The small number of employees living in the close proximity of the new workplace location will reach it by foot or by bicycle. The majority of the employees, namely those who were used to drive to the old location, will suffer the most from the relocation. As it is no longer possible to reach the new workplace by car, they will choose intermodal transport, which involves car and public transport (park & ride). The focus group results suggested that the intermodal transport choice will be more widely used on the short-term, whereas the mode split scenario provided by the discrete choice model should be achieved after a period of adjustment. As already mentioned, walking and cycling mode were not included in the MNL. All the 143 employees of the firm to be relocated were interviewed. Table 6 shows the comparison between focus group revealed intention and model results.
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